Environmental Modeling & Assessment

, Volume 19, Issue 5, pp 407–424 | Cite as

Simulating Unstressed Crop Development and Growth Using the Unified Plant Growth Model (UPGM)

  • Gregory S. McMaster
  • James C. AscoughII
  • Debora A. Edmunds
  • Larry E. Wagner
  • Fred A. Fox
  • Kendall C. DeJonge
  • Neil C. Hansen
Article

Abstract

Since initial development of the EPIC model in 1989, the EPIC plant growth component has been incorporated into other erosion and crop management models (e.g., WEPS, WEPP, SWAT, ALMANAC, and GPFARM) and subsequently modified to meet research objectives of the model developers. This has resulted in different versions of the same base plant growth component. The objectives of this study are the following: (1) describe the standalone Unified Plant Growth Model (UPGM), initially derived from the WEPS plant growth model, to be used for merging enhancements from other EPIC-based plant growth models; and (2) describe and evaluate new phenology, seedling emergence, and canopy height sub-models derived from the Phenology Modular Modeling System (PhenologyMMS V1.2) and incorporated into UPGM. A 6-year (2005–2010) irrigated maize (Zea mays L.) study from northeast Colorado was used to calibrate and evaluate UPGM running both the original (i.e., based on WEPS) and new phenology, seedling emergence, and canopy height sub-models. Model statistics indicated the new sub-models usually resulted in better simulation results than the original sub-models. For example when comparing original and new sub-models, respectively, for predicting canopy height, the root mean square error (RMSE) was 53.7 and 40.7 cm, index of agreement (d) was 0.84 and 0.92, relative error (RE) was 26.0 and −1.26 %, and normalized objective function (NOF) was 0.47 and 0.33. The new sub-models predict leaf number (old sub-models do not), with mean values for 4 years of 2.43 leaves (RMSE), 0.78 (d), 18.38 % (RE), and 0.27 (NOF). Simulating grain yield, final above ground biomass, and harvest index showed little difference when running the original or new sub-models. Both the new phenology and seedling emergence sub-models respond to varying water deficits, increasing the robustness of UPGM for more diverse environmental conditions. Future research will continue working to incorporate existing enhancements from other EPIC-based plant growth models to unify them into one model such as multispecies competition and N cycling.

Keywords

Seedling emergence Phenology Canopy height Growth stages Leaf production Simulation model 

References

  1. 1.
    Armbrust, D. V., & Retta, A. (2000). Wind and sandblast damage to growing vegetation. Annals of Arid Zone, 39, 273–284.Google Scholar
  2. 2.
    Arnold, J. G., Weltz, M. A., Alberts, E. E., & Flanagan, D. C. (1995). Plant growth component. In D. C. Flanagan, M. A. Nearing, & J. M. Laflen (Eds.), USDA-Water Erosion Prediction Project: hillslope profile and watershed model documentation, NSERL report no. 10 (pp. 8.1–8.41). West Lafayette: USDA-ARS National Soil Erosion Research Laboratory.Google Scholar
  3. 3.
    Ascough, J. C., II, McMaster, G. S., Andales, A. A., Hansen, N. C., & Sherrod, L. A. (2007). Evaluating GPFARM crop growth, soil water, and soil nitrogen components for Colorado dryland locations. Transactions of the American Society of Agricultural and Biological Engineers, 50, 1565–1578.Google Scholar
  4. 4.
    Cabelguenne, M., Jones, C. A., Marty, J. R., Dyke, P. T., & Williams, J. R. (1990). Calibration and validation of EPIC for crop rotations in southern France. Agricultural Systems, 33(2), 153–171.CrossRefGoogle Scholar
  5. 5.
    Cabelguenne, M., Jones, C. A., & Williams, J. R. (1995). Strategies for limited irrigations of maize in southwestern France—a modeling approach. Transactions of the American Society of Agricultural Engineers, 38(2), 507–511.CrossRefGoogle Scholar
  6. 6.
    Cabelguenne, M., Debaeke, P., Puech, J., & Bosc, N. (1997). Real time irrigation management using the EPIC-PHASE model and weather forecasts. Agricultural Water Management, 32, 227–238.CrossRefGoogle Scholar
  7. 7.
    Cabelguenne, M., Debaeke, P., & Bouniols, A. (1999). EPICphase, a version of the EPIC model simulating the effects of water and nitrogen stress on biomass and yield, taking account of developmental stages: validation on maize, sunflower, sorghum, soybean, and winter wheat. Agricultural Systems, 60(3), 175–196.CrossRefGoogle Scholar
  8. 8.
    Carberry, P. S., Muchow, R. C., & Hammer, G. L. (1993). Modelling genotypic and environmental control of leaf area dynamics in grain sorghum. II. Individual leaf level. Field Crops Research, 33, 311–328.CrossRefGoogle Scholar
  9. 9.
    Cavero, J., Plant, R. E., Shennan, C., Friedman, D. B., Williams, J. R., Kiniry, J. R., et al. (1999). Modeling nitrogen cycling in tomato–safflower and tomato–wheat rotations. Agricultural Systems, 60, 123–135.CrossRefGoogle Scholar
  10. 10.
    Colorado corn variety performance trials–making better decisions. (2005–11). http://www.aes.colostate.edu/Pubs/CAES_Pub_List_4Web.html and select year and crop. Verified 30 Sept 2013.
  11. 11.
    Constable, G. A., & Rawson, H. M. (1980). Effect of leaf position, expansion and age on photosynthesis, transpiration and water use efficiency. Australian Journal of Plant Physiology, 7, 89–100.CrossRefGoogle Scholar
  12. 12.
    Deer-Ascough, L. A., McMaster, G. S., Ascough, J. C., II, & Peterson, G. A. (1998). Applications of generic crop growth model technology to Great Plains conservation systems. ASAE Paper No. 982133. Presented at the ASAE Annual International Meeting. St. Joseph: ASAE.Google Scholar
  13. 13.
    DeJonge, K. C., Andales, A. A., Ascough, J. C., II, & Hansen, N. C. (2011). Modeling of full and limited irrigation scenarios for corn in a semiarid environment. Transactions of the American Society of Agricultural and Biological Engineers, 54, 481–492.Google Scholar
  14. 14.
    Escudero, A., & Mediavilla, S. (2003). Decline in photosynthetic nitrogen use efficiency with leaf age and nitrogen resorption as determinants of leaf life span. Journal of Ecology, 91, 880–889.CrossRefGoogle Scholar
  15. 15.
    Flanagan, D. C., Nearing, M. A., & Laflen, J. M. (Eds.). (1995). USDA-Water Erosion Prediction Project Hillslope Profile and Watershed Model Documentation. NSERL report no. 10. West Lafayette, IN: USDA-ARS National Soil Erosion Research Laboratory.Google Scholar
  16. 16.
    Funk, R., Skidmore, E. L., & Hagen, L. J. (2004). Comparison of wind erosion measurements in Germany with simulated soil losses by WEPS. Environmental Modelling & Software, 19, 177–183.CrossRefGoogle Scholar
  17. 17.
    Hagen, L. J. (1991). A wind erosion prediction system to meet user needs. Journal of Soil and Water Conservation, 46(2), 105–111.Google Scholar
  18. 18.
    Hagen, L. J. (2004). Evaluation of the Wind Erosion Prediction System (WEPS) erosion submodel on cropland fields. Environmental Modelling and Software, 19(2), 171–176.CrossRefGoogle Scholar
  19. 19.
    Hagen, L. J., Van Pelt, S., Zobeck, T. M., & Retta, A. (2007). Dust deposition near an eroding source field. Earth Surface Processes and Landforms, 32, 281–289.CrossRefGoogle Scholar
  20. 20.
    He, X., Izaurralde, R. C., Vanotti, M. B., Williams, J. R., & Thomson, A. M. (2006). Simulating long-term and residual effects of nitrogen fertilization on corn yields, soil carbon sequestration, and soil nitrogen dynamics. Journal of Environmental Quality, 35, 1608–1619.CrossRefGoogle Scholar
  21. 21.
    Huang, M., Gallichand, J., Dang, T., & Shao, M. (2006). An evaluation of EPIC soil water and yield components in the gully region of Loess Plateau, China. Journal of Agricultural Science. doi:10.1017/S0021859606006101.Google Scholar
  22. 22.
    Jantalia, C. P., & Halvorson, A. D. (2011). Nitrogen fertilizer effects on irrigated conventional tillage corn yields and soil carbon and nitrogen pools. Agronomy Journal, 103, 871–878.CrossRefGoogle Scholar
  23. 23.
    Kang, S., Gu, B., Du, T., & Zhang, J. (2003). Crop coefficient and ratio of transpiration to evaporation of winter wheat and maize in a semi-humid region. Agricultural Water Management, 59, 239–254.CrossRefGoogle Scholar
  24. 24.
    Kiniry, J. R., Blanchet, R., Williams, J. R., Texier, V., Jones, C. A., & Cabelguenne, M. (1992). Sunflower simulation using the EPIC and ALMANAC models. Field Crop Research, 30(3–4), 403–423.CrossRefGoogle Scholar
  25. 25.
    Kiniry, J. R., Williams, J. R., Gassman, P. W., & Debaeke, P. (1992). A general process-oriented model for two competing plant species. Transactions of the American Society of Agricultural Engineers, 35, 801–810.CrossRefGoogle Scholar
  26. 26.
    Ko, J., Piccinni, G., & Steglich, E. (2009). Using EPIC to manage irrigated cotton and maize. Agricultural Water Management, 96(9), 1323–1331.CrossRefGoogle Scholar
  27. 27.
    Liu, T., Song, F., Liu, S., & Zhu, X. (2012). Light interception and radiation use efficiency response to narrow-wide row planting patterns in maize. Australian Journal of Crop Science, 6, 506–513.Google Scholar
  28. 28.
    Lorenz, A. J., Gustafson, T. J., Coors, J. G., & de Lon, N. (2010). Breeding maize for a bioeconomy: a literature survey examining harvest index and stover yield and their relationship to grain yield. Crop Science, 50, 1–12.CrossRefGoogle Scholar
  29. 29.
    McMaster, G. S., Ascough, J. C., II, Dunn, G. A., Weltz, M. A., Shaffer, M., Palic, D., et al. (2002). Application and testing of GPFARM: a farm and ranch decision support system for evaluating economic and environmental sustainability of agricultural enterprises. Acta Horticulture, 593, 171–177.Google Scholar
  30. 30.
    McMaster, G. S., Ascough, J. C., II, Edmunds, D. A., Nielsen, D. C., & Prasad, P. V. V. (2013). Simulating crop phenological responses to water stress using the PhenologyMMS software component. Applied Engineering in Agriculture, 29, 233–249.CrossRefGoogle Scholar
  31. 31.
    McMaster, G. S., Ascough, J. C., II, Nielsen, D. C., Byrne, P. F., Haley, S. D., Shaffer, M. J., et al. (2003). Using species-based plant parameters in GPFARM: complications of varieties and the G x E interaction in wheat. Transactions of the American Society of Agricultural Engineers, 46, 1337–1346.CrossRefGoogle Scholar
  32. 32.
    McMaster, G. S., Edmunds, D. A., Wilhelm, W. W., Nielsen, D. C., Prasad, P. V. V., & Ascough, J. C., II. (2011). PhenologyMMS: a program to simulate crop phenological responses to water stress. Computers and Electronics in Agriculture, 77, 118–125.CrossRefGoogle Scholar
  33. 33.
    McMaster, G. S., Klepper, B., Rickman, R. W., Wilhelm, W. W., & Willis, W. O. (1991). Simulation of shoot vegetative development and growth of unstressed winter wheat. Ecological Modelling, 53, 189–204.CrossRefGoogle Scholar
  34. 34.
    McMaster, G. S., White, J. W., Weiss, A., Baenziger, P. S., Wilhelm, W. W., Porter, J. R., et al. (2008). Simulating crop phenological responses to water deficits. In L. R. Ahuja, V. R. Reddy, S. S. Anapalli, & Q. Yu (Eds.), Modeling the response of crops to limited water: recent advances in understanding and modeling water stress effects on plant growth processes. Advances in agricultural systems modeling. Trans-disciplinary research, synthesis, and applications (Vol. 1, pp. 277–300). Madison: ASA-SSSA-CSSA.Google Scholar
  35. 35.
    McMaster, G. S., & Wilhelm, W. W. (1997). Growing degree-days: one equation, two interpretations. Agricultural and Forest Meteorology, 87, 289–298.CrossRefGoogle Scholar
  36. 36.
    McMaster, G. S., & Wilhelm, W. W. (2003). Phenological responses of wheat and barley to water and temperature: improving simulation models. Journal of Agricultural Science (Cambridge), 141, 129–147.CrossRefGoogle Scholar
  37. 37.
    McMaster, G. S., Wilhelm, W. W., & Frank, A. B. (2005). Developmental sequences for simulating crop phenology for water-limiting conditions. Australian Journal of Agricultural Research, 56, 1277–1288.CrossRefGoogle Scholar
  38. 38.
    McMaster, G. S., Wilhelm, W. W., & Morgan, J. A. (1992). Simulating winter wheat shoot apex phenology. Journal of Agricultural Science (Cambridge), 119, 1–12.CrossRefGoogle Scholar
  39. 39.
    Miralles, D. J., & Slafer, G. A. (1997). Radiation interception and radiation use efficiency of near-isogenic wheat lines with different height. Euphytica, 97, 201–208.CrossRefGoogle Scholar
  40. 40.
    Monsi, M., & Saeki, T. (1953). Uber den Lictfaktor in den Pflanzengesell – Schaften und sein Bedeutung fur die Stoffproducktion. Japanese Journal of Botany, 14, 22–52.Google Scholar
  41. 41.
    Monteith, J. L. (1977). Climate and the efficiency of crop production in Britain. Philosophical Transactions Research Society of London B, 281, 277–329.CrossRefGoogle Scholar
  42. 42.
    Nielsen, D. C., & Hinkle, S. E. (1996). Field evaluation of basal crop coefficients for corn based on growing degree days, growth stage, or time. Transactions of the American Society of Agricultural Engineers, 39, 97–103.CrossRefGoogle Scholar
  43. 43.
    Retta, A., & Armbrust, D. V. (1995). Estimation of leaf and stem area in the Wind Erosion Prediction System (WEPS). Agronomy Journal, 87, 93–98.CrossRefGoogle Scholar
  44. 44.
    Retta, A., Armbrust, D. V., & Hagen, L. J. (1996). Partitioning of biomass in the crop sub-model of WEPS (Wind Erosion Prediction System). Transactions of the American Society of Agricultural Engineers, 39, 145–151.CrossRefGoogle Scholar
  45. 45.
    Retta, A., Armbrust, D. V., Hagen, L. J., & Skidmore, E. L. (2000). Leaf and stem area relationships to masses and their height distribution in native grasses. Agronomy Journal, 92, 225–230.CrossRefGoogle Scholar
  46. 46.
    Rosenberg, N. J., McKenney, M. S., Easterling, W. E., & Lemon, K. M. (1992). Validation of EPIC model simulations of crop responses to current climate and CO2 conditions: comparisons with census, expert judgment and experimental plot data. Agricultural and Forest Meteorology, 59, 35–51.CrossRefGoogle Scholar
  47. 47.
    Van Ittersum, M. K., Leffelaar, P. A., Van Keulen, H., Kropff, M. J., Bastiaans, L., & Goudriaan, J. (2003). On approaches and applications of the Wageningen crop models. European Journal of Agronomy, 18(3–4), 201–234.CrossRefGoogle Scholar
  48. 48.
    van Keulen, H., Penning de Vries, F. W. T., & Drees, E. M. (1982). A summary model for crop growth. In F. W. T. Penning de Vries & H. H. van Laar (Eds.), Simulation of plant growth and crop production. Simulation monographs. Wageningen: Pudoc.Google Scholar
  49. 49.
    Van Oosterum, E. J., Carberry, P. S., & O’Leary, G. J. (2001). Simulating growth, development, and yield of tillering pearl millet. I. Leaf area profiles on main shoots and tillers. Field Crops Research, 72, 51–66.CrossRefGoogle Scholar
  50. 50.
    Vorst, J.J. (1993). Assessing hail damage to corn. Publication NCH-1, West Lafayette, Ind.: Purdue University Cooperative Extension Service. Available at: www.ces.purdue.edu/extmedia/NCH/NCH-1.html (verified 30 September 2013).
  51. 51.
    Wagner, L.E. (1996). An overview of the wind erosion prediction system. Proc. International Conference on Air Pollution from Agricultural Operations, Feb. 7–9, 1996, Kansas City, Missouri, pp. 73–75.Google Scholar
  52. 52.
    Wagner, L. E. (2013). A history of wind erosion prediction models in the United States Department of Agriculture: the Wind Erosion Prediction System (WEPS). Aeolian Research Journal, 10, 9–24.CrossRefGoogle Scholar
  53. 53.
    Wang, X., Gassman, P. W., Williams, J. R., Potter, S., & Kemanian, A. R. (2008). Modeling the impacts of soil management practices on runoff, sediment yield, maize productivity, and soil organic carbon using APEX. Soil and Tillage Research, 101(1–2), 78–88.CrossRefGoogle Scholar
  54. 54.
    WEPS User Manual. (2010). www.ars.usda.gov/services/software/download.htm?softwareid=415 Verified 30 September 2013.
  55. 55.
    Wilhelm, W. W., McMaster, G. S., Rickman, R. W., & Klepper, B. (1993). Above-ground vegetative development and growth of winter wheat as influenced by nitrogen and water availability. Ecological Modelling, 68, 183–203.CrossRefGoogle Scholar
  56. 56.
    Williams, J. R., Jones, C. A., Kiniry, J. R., & Spanel, D. A. (1989). The EPIC crop growth model. Transactions of the American Society of Agricultural Engineers, 32, 497–511.CrossRefGoogle Scholar
  57. 57.
    Xie, Y., Kiniry, J. R., Nedbalek, V., & Rosenthal, W. D. (2001). Maize and sorghum simulations with CERES_Maize, SORKAM, and ALMANAC under water-limiting conditions. Agronomy Journal, 93, 1148–1155.CrossRefGoogle Scholar
  58. 58.
    Zhu, X.-G., Long, S. P., & Ort, D. R. (2010). Improving photosynthetic efficiency for greater yield. Annual Review of Plant Biology, 61, 235–261.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland (outside the USA) 2014

Authors and Affiliations

  • Gregory S. McMaster
    • 1
  • James C. AscoughII
    • 1
  • Debora A. Edmunds
    • 1
  • Larry E. Wagner
    • 2
  • Fred A. Fox
    • 2
  • Kendall C. DeJonge
    • 3
  • Neil C. Hansen
    • 4
  1. 1.Agricultural Systems Research UnitUSDA-ARS-NPAFort CollinsUSA
  2. 2.Engineering & Wind Erosion Research UnitUSDA-ARS-NPAManhattanUSA
  3. 3.Water Management Research UnitUSDA-ARS-NPAFort CollinsUSA
  4. 4.Department of Plant and Wildlife SciencesBrigham Young UniversityProvoUSA

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