Plant and Soil

, Volume 229, Issue 1, pp 71–82 | Cite as

N rate and transport under variable cropping history and fertilizer rate on loamy sand and clay loam soils: II. Performance of LEACHMN using different calibration scenarios

  • J.M. Sogbedji
  • H.M. van Es
  • J.L. Hutson
  • L.D. Geohring
Article

Abstract

Testing of existing agronomic models is needed to ensure their validity and applicability to different soils, cropping systems and environments. Data collected from a 3-year field experiment of maize (zea mays L.) on a loamy sand and a clay loam soil were used to validate the research version of the LEACHMN model for water flow and N fate and transport. Three calibration scenarios with increasing levels of generalization for transformation rate coefficients were used based on: (i) each year, treatment and soil type (ii) 3-year average values for each treatment and soil type, and (iii) average over years and soil types. Model accuracy was tested using both graphical and statistical methods including 1:1 scale plot, root mean square error and normalized root mean square error, and correlation coefficient values. The model accurately predicted drainage water flow rate and volume under both sites. Calibrated N transformation rate constants for each treatment, year and soil type provided satisfactory predictions of growing season cumulative NO3–N leaching losses, and accurate predictions of growing season cumulative maize N uptake at both sites. The use of 3-year average rate constant values for each site resulted in fairly satisfactory predictions of NO3–N leaching losses on the clay loam site, but inaccurate predictions on the loamy sand site. The model provided accurate predictions of cumulative maize N uptake for both sites. Using the rate constant values averaged over years and soil types resulted mostly in inaccurate predictions. Use of year and soil type-specific N rate coefficients results in accurate LEACHMN predictions of N leaching and maize N uptake. When rate coefficients are generalized over years for each soil type, satisfactory model predictions may be expected when N dynamics are not strongly affected by yearly variations in organic N inputs.

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References

  1. Addiscott TMI and Wagenet R J 1985 Concepts of solute leaching in soils: A review of modeling approaches. J. Soil Sci. 36, 411–424.Google Scholar
  2. Addiscott T M and Whitmore A P 1987 Computer simulation of changes in soil mineral nitrogen and crop nitrogen during autumn, winter and spring. J. Agri. Sci. Camb. 109, 141–157.Google Scholar
  3. Addiscott T M, Whitmore A P and Powlson D S 1991 Farming, fertilizer, and the nitrate problem. C.A.B International, Wallingford, UK.Google Scholar
  4. Ahuja L R, DeCoursey D G, Barnes B B and Rojas K W 1991 Characteristics and importance of preferential macropore transport studied with the ARS root zone water quality model. In Preferential Flow: Proceeding of the National Symposium, 16- 17 December, 1991, Chicago, USA. Eds. T J Gish and A Shirmohammadi, pp. 32–49. American Society of Agricultural Engineers, St. Joseph, MI, USA.Google Scholar
  5. Aisenbrey Jr, Hayes R B, Warren H J, Winsett D L and Young R B 1974 Design of small canal structures. United States Department of the Interior, Burea of Reclamation. Denver, Colorado.Google Scholar
  6. Clay D E, Clapp C B, Linden D R and Molina J A B 1985a Nitrogentillage-residue-management. I. Simulating soil and plant behavior by the model NCSWAP. Plant Soil 84, 67–77.Google Scholar
  7. Cornell Nutrient Analysis Laboratories 1987 Phosphorus and nitrate calorimetric determinations by autoanlyzer. In Methods for Soil Fertility Analysis. Eds. M C McCenahan and G A Ferguson. pp 7–16. Procedure No s1101. Cornell Univ. Ithaca, NY.Google Scholar
  8. Cornell Nutrient Analysis Laboratories 1989 Extraction of soil for ‘available’ nutrients. In Methods for Soil Fertility Analysis. Eds. M C McCenahan and G A Ferguson. pp 3–6. Procedure No s1100. Cornell Univ. Ithaca, NY.Google Scholar
  9. Donnigan A S Jr 1983 Model predictions vs. field observations: The model validation/testing process. In Fate of Chemicals in the Environment. Eds. R I Swann and A Eschenroder. pp 151–171. A.C.S Symposium Series 225. Am. Chem. Soc., Washington, D.C.Google Scholar
  10. Hutson J L and Wagenet R J 1992 LEACHM Leaching Estimation And Chemistry Model: A process-based model of water and solute movement, transformations, plant uptake, and chemical reactions in the unsaturated zone. Version 3. Dept. of Soil, Crop and Atmospheric Sciences. Research series 92-3. Cornell Univ., Ithaca, NY.Google Scholar
  11. Jabro J D, Lotse J D, Fritton D D and Baker D E 1994 Estimation of preferential movement of bromide tracer under field conditions. J. Hydrol. 156, 61–71.Google Scholar
  12. Jemison J M Jr 1991 Nitrate leaching from soil measured with zerotension pan lysimeters as influenced by nitrogen fertilizer rate and manure application: Field estimates and model predictions. Ph.D. diss. Pennsylvania State Univ, Univ. Park.Google Scholar
  13. Jemison J M Jr, Jabro J D and Fox R H 1994 LEACHM evaluation: I. Simulation of cumulative drainage, bromide leaching and corn bromide uptake. Agron. J. 86, 843–851.Google Scholar
  14. Johnsson H, Bergstrom L, Jansson P and Paustian K 1987 Simulated nitrogen dynamics and losses in a layered agricultural soil. Agric. Ecosystem Environ. 18, 333–3336.Google Scholar
  15. Jury WA, Sposito G and White R E 1986 A transfer function model of solute transport in soils: I. fundamental concepts. Wat. Resour. Res. 22, 243–247.Google Scholar
  16. Loague K and Green R E 1991 Statistical and graphical methods for evaluating solute transport models: Overview and application. J. Contam. Hydrol. 7, 51–73.Google Scholar
  17. Magdoff F R 1991 Understanding the corn pre-sidedress nitrate test: A step towards environmentally sound N management. J. Prod. Agric. 4, 297–305.Google Scholar
  18. Nofziger D L and Hornsby A G 1986 A microcomputer-based management tool for chemical movement in soil. Appl. Agric. Res. 1, 50–56.Google Scholar
  19. SAS Institute 1996 SAS user's guide version 6 SAS Inst., Cary, NC.Google Scholar
  20. Smith W N, Reynolds W D, De Jong R, Clemente R S and Topp B 1995 Water flow through intact soil columns: Measurement and simulation using LEACHMN. J. Environ. Qual. 24: 874–891.Google Scholar
  21. Sogbedji J M, van Es H M, Yang C L, Geohring L D and Magdoff F R 2000a Nitrate Leaching and Nitrogen Mass Balance under Maize as Affected by Nitrogen Management Practices and Soil Type. J. Environ. Qual. 29: 1813–1820.Google Scholar
  22. Sogbedji J M, van Es H M and Hutson J L 2000b N fate and transport under variable cropping history and fertilizer rate on loamy sand and clay loam soils: I. Calibration of the LEACHMN model. Plant Soil (this issue).Google Scholar
  23. Wagenet R J and Huston J L 1989 LEACHiM: leaching estimation and chemistry model. Ver. 2. Cornell University. Ithaca, NY.Google Scholar
  24. Whitmore A P and Addiscott T MI 1987 Applications of computer modeling to predict mineral nitrogen in soil and nitrogen in crops. Soil Use Manage. 3, 38–43.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • J.M. Sogbedji
    • 1
    • 2
  • H.M. van Es
    • 1
  • J.L. Hutson
    • 1
  • L.D. Geohring
    • 1
  1. 1.Dep. of Crop and Soil Sci., and Dep. of Agric. and Biol Engin.Cornell Univ.Ithaca
  2. 2.J.L. Hutson, School of Earth Sci., The Flinders University of South AustraliaAdelaideSouth Australia

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