Hostname: page-component-6766d58669-76mfw Total loading time: 0 Render date: 2026-05-21T19:52:25.854Z Has data issue: false hasContentIssue false
  • English
  • Français

Software Tools for Weed Seed Germination Modeling

Published online by Cambridge University Press:  20 January 2017

Kurt Spokas  and
Frank Forcella
Kurt Spokas*
Affiliation:
U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) Soil and Water Management Unit, St. Paul, MN 55108 and University of Minnesota, Department of Soil, Water and Climate, St. Paul, MN 55108
Frank Forcella
Affiliation:
USDA-ARS North Central Soil Conservation Research Laboratory, 803 Iowa Ave, Morris, MN 56267
*
Corresponding author's E-mail: kurt.spokas@ars.usda.gov
Get access Rights & Permissions [Opens in a new window]

Abstract

The next generation of weed seed germination models will need to account for variable soil microclimate conditions. To predict this microclimate environment we have developed a suite of individual tools (models) that can be used in conjunction with the next generation of weed seed germination models. The three tools that will be outlined here are GlobalTempSIM, GlobalRainSIM, and the soil temperature and moisture model (STM2). Each model was compared with several sets of observed data from worldwide locations. Overall, the climate predictors compared favorably. GlobalTempSIM had a bias between −2.7 and +0.9 C, mean absolute errors between 1.9 and 5.0 C, and an overall Willmott d-index of 0.79 to 0.95 (where d = 1 represents total agreement between observed and modeled data) for 12 global validation sites in 2007. GlobalRainSIM had a bias for cumulative precipitation ranging from −210 to +305 mm, a mean absolute error between 29 and 311 mm, and a corresponding d-index of 0.78 to 0.99 for the sites and years compared. The high d-indices indicate that the models adequately captured the annual patterns for the validation sites. STM2 also performed well in comparisons with actual soil temperatures with a range of −2 to +4.6 C biases and mean absolute errors between 0.7 and 6.8 C, with the d-index ranging from 0.83 to 0.99 for the soil temperature comparisons. The soil moisture prediction annual bias was between −0.09 and +0.12 cm3 cm−3, mean absolute errors ranging from 0.02 to 0.16 cm3 cm−3, and possessed a d-index between 0.32 and 0.91 for the validation sites. These models were developed in JAVA, are simple to use, operate on multiple platforms (e.g., Mac, personal computer, Sun), and are freely available for download from the U.S. Department of Agriculture Agricultural Research Service website (http://www.ars.usda.gov/Services/docs.htm?docid=11787).

Keywords

Environmental modelingclimatesimulationJAVA

Information

Type
Special Topics
Information
Weed Science , Volume 57 , Issue 2 , April 2009 , pp. 216 - 227
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Literature Cited

Ács, F., Mihailovi, D. T., and Rajkovi, B. 1991. A coupled soil moisture and surface temperature prediction model. J. Appl. Meteorol. 30:812–822.Google Scholar
Airey, M. and Hulme, M. 1995. Evaluating climate model simulations of precipitation: methods, problems and performance. Phys. Geogr. 19:427–448. DOI: 10.1177/030913339501900401.Google Scholar
Bridges, D. C. 1992. Crop Losses Due to Weed in the United States. Champaign, IL Weed Science Society of America.Google Scholar
Bull, J. M., Smith, L. A., Ball, C., Pottage, L., and Freeman, R. 2003. Benchmarking Java against C and Fortran for scientific applications. Concur. Computat. Pract. Exp. 15:417–430 (DOI: 10.1002/cpe.658).Google Scholar
Campbell, G. S. 1985. Soil Physics with BASIC. New York Elsevier. 150.Google Scholar
Cesaraccio, C., Spano, D., Duce, P., and Snyder, R. L. 2001. An improved model for degree-days form daily temperature data. Int. J. Biomet. 45:161–169.Google Scholar
Chancellor, R. J. 1985. Tillage effects of annual weed germination. In: Shibles, R. Proceedings of the World Soybean Research Conference III. Boulder, CO Westview.Google Scholar
Chanzy, A., Mumen, M., and Richard, G. 2008. Accuracy of top soil moisture simulation using a mechanistic model with limited soil characterization. Water Resour. Res. 44:W03432. DOI:10.1029/2006WR005765.CrossRefGoogle Scholar
Cousens, R. 1985. An empirical model relating crop yield to weed and crop density and a statistical comparison with other models. J. Agric. Sci. 105:513–521.CrossRefGoogle Scholar
Cox, W. J., Hahn, R. R., Stachowski, P. J., and Cherney, J. H. 2005. Weed interference and glyphosate timing affect corn forage yield and quality. Agron. J. 97:847–853.Google Scholar
Daly, C., Neilson, R. P., and Phillips, D. L. 1994. A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl.Meteoroll. 33:140–158.2.0.CO;2>CrossRefGoogle Scholar
de Vries, D. A. 1963. Thermal properties of soils. Pages 210–235. in van Wijk, W. R. Physics of Plant Environments Amsterdam. North-Holland.Google Scholar
Diekkrüger, B., Söndgerath, D., Kersenbaum, K. C., and McVoy, C. W. 1995. Validity of agroecosystem models: a comparison of results of different models applied to the same data set. Ecol. Model. 81:3–29.CrossRefGoogle Scholar
Dill, G. M. 2005. Glyphosate-resistant crops: history, status and future. Pest Manag. Sci. 61:219–224.CrossRefGoogle ScholarPubMed
Dubrovský, M., Buchtele, J., and Žalud, Z. 2004. High-frequency and low-frequency variability in stochastic daily weather generator and its effect on agricultural and hydrologic modelling. Clim. Change. 63:145–179.Google Scholar
Eitzinger, J., Trnka, M., Hösch, J., Alud, Z., and Dubrovský, M. 2004. Comparison of CERES, WOFOST and SWAP models in simulating soil water content during growing season under different soil conditions. Ecol. Model. 171:223–246.Google Scholar
Evett, S. R., Tolk, J. A., and Howell, T. A. 2006. Soil profile water content determination: sensor accuracy, axial response, calibration, temperature dependence and precision. Vadose Zone J. 5:894–907.CrossRefGoogle Scholar
Farouki, O. T. 1986. Thermal Properties of Soils. Series on Rock and Soil Mechanics, Volume 11. Clausthal-Zellerfeld, Germany Trans Tech. 136.Google Scholar
Fayer, M. J. 2000. UNSAT-H version 3.0: Unsaturated soil water and heat flow model, theory, user manual, and examples. PNNL 13249. Pacific Northwest National Laboratory, Richland, WA.Google Scholar
Federer, C. A. 1983. Nitrogen mineralization and nitrification: depth variation in four New England forest soils. Soil Sci. Soc. Am. J. 47:1008–1014.Google Scholar
Finke, P. A., Wösten, J. H. M., and Jansen, M. J. W. 1996. Effects of uncertainty in major input variables on simulated functional soil behaviour. Hydrol. Proc. 10:661–669.Google Scholar
Flerchinger, G. N. 1987. Simultaneous Heat and Water Model of a Snow-Residue-Soil System. Ph.D. dissertation. Pullman, WA: Washington State University.Google Scholar
Flerchinger, G. N., Kustas, W. P., and Weltz, M. A. 1998. Simulating surface energy fluxes and radiometric surface temperatures for two arid vegetation communities using the SHAW model. J. Appl. Meteorol. 37:449–460.2.0.CO;2>CrossRefGoogle Scholar
Fox, G. C. and Furmanski, W. 1998. Java for parallel computing and as a general language for scientific and engineering simulation and modeling. Concur. Pract. Exp. 9 (6):415–425.Google Scholar
Gudenberg, J. W. V. 1998. Java for scientific computing, pros and cons. J Univ. Comput. Sci. 4 (1):11–15.Google Scholar
Hammel, J. E., Papendick, R. I., and Campbell, G. S. 1981. Fallow tillage effects on evaporation and seedzone water content in a dry summer climate. Soil Sci. Soc. Am. J. 45:1016–1022.Google Scholar
Hanson, C. L., Cumming, K. A., Woolhiser, D. A., and Richardson, C. W. 1994. Microcomputer Program for Daily Weather Simulation in the Contiguous United States. USDA-ARS #114.Google Scholar
Harmel, R. D., Richardson, C. W., Hanson, C. L., and Johnson, G. L. 2002. Evaluating the adequacy of simulating maximum and minimum daily air temperature with the normal distribution. J. Appl. Meteorol. 41:744–753.Google Scholar
Holm, L., Doll, J., Holm, E., Pancho, J., and Herberger, J. 1997. World Weeds: Natural Histories and Distribution. New York John Wiley & Sons. 1129.Google Scholar
Holm, L., Plucknett, D., Pancho, J., and Herberger, J. 1977. The World's Worst Weeds: Distribution and Biology. Honolulu University of Hawaii Press. 609.Google Scholar
Hymer, D. C., Moran, M. S., and Keefer, T. O. 2000. Soil water evaluation using a hydrologic model and calibrated sensor network. Soil Sci. Soc. Am. J. 64:319–326.Google Scholar
Irmak, S. and Haman, D. Z. 2001. Performance of the Watermark granular matrix sensor in sandy soils. Appl. Eng. Agric. 17:787–795.Google Scholar
Johnson, M. J., Mayers, C. J., Garcia, C. A., and Andraski, B. J. 2007. Selected micrometeorological, soil-moisture, and evapotranspiration data at Amargosa Desert Research Site in Nye County near Beatty, Nevada, 2001–05. U.S. Geological Survey Data Series 284. 28.Google Scholar
Kaur, R., Kumar, S., and Gurung, H. P. 2002. A pedo-transfer function (PTF) for estimating soil bulk density from basic soil data and its comparison with existing PTFs. Austr. J. Soil Res. 40:847–857.Google Scholar
Kleinhenz, M. D. and Cardina, J. 2003. Compost application effects on weed populations and crop yield and quality in three early-maturing, organically managed potato (Solanum tuberosum) cultivars. Acta Hort. (ISHS) 619:337–343.CrossRefGoogle Scholar
Lamour, A. and Lotz, L. A. P. 2007. The importance of tillage depth in relation to seedling emergence in stale seedbeds. Ecol. Model. 201:536–546.Google Scholar
Legates, D. R. and McCabe, G. J. 1999. Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35:233–241.CrossRefGoogle Scholar
Legates, D. R. and Willmott, C. J. 1990a. Mean seasonal and spatial variability global surface air temperature. Theor. Appl. Climatol. 41:11–21.Google Scholar
Legates, D. R. and Willmott, C. J. 1990b. Mean seasonal and spatial variability in gauge-corrected global precipitation. Int. J. Climatol. 10:111–127.Google Scholar
Leib, B. G., Jabro, J. D., and Matthews, G. R. 2003. Field evaluation and performance comparison of soil moisture sensors. Soil Sci. 168:396–408.CrossRefGoogle Scholar
Leonavičiutė, N. 2000. Predicting soil bulk and particle densities by pedotransfer functions from existing soil data in Lithuania. Geografijos Metraštis. 33:317–330.Google Scholar
Lindquist, J. L., Rhode, D., Puettmann, K. J., and Maxwell, B. D. 1994. The influence of plant population spatial arrangement on individual plant yield. Ecol. Appl. 4:518–524.Google Scholar
Mayer, D. G. and Butler, D. G. 1993. Statistical validation. Ecol. Model. 68:21–32.Google Scholar
McGiffin, M., Spokas, K. A., Forcella, F., Archer, D. W., Poppe, S., and Figueroa, R. 2008. Emergence prediction of common groundsel (Senecio vulgaris). Weed Sci. 56:58–65.CrossRefGoogle Scholar
Nagai, H. 2002. Validation and sensitivity analysis of a new atmosphere–soil–vegetation model. J. Appl. Meteorol. 41:160–176.2.0.CO;2>CrossRefGoogle Scholar
Neve, P., Diggle, A. J., Smith, F. P., and Powles, S. B. 2003. Simulating evolution of glyphosate resistance in Lolium rigidum I: Population biology of a rare resistance trait. Weed Res. 43:404–417.Google Scholar
New, M., Hulme, M., and Jones, P. D. 1999. Representing twentieth century space-time climate variability. Part 1: Development of a 1961–90 mean monthly terrestrial climatology. J. Clim. 12:829–856.Google Scholar
Nicks, A. D. and Lane, L. J. 1989. Weather generator. USDA-Water Erosion Prediction Project: Hillslope Profile Model Documentation. USDA-ARS NSERL Rep. 2.Google Scholar
Nicks, A. D., Richardson, C. W., and Williams, J. R. 1990. Evaluation of the EPIC model weather generator. Erosion/productivity impact calculator, 1. Model documentation. in Sharpley, A. N. and Williams, J. R. USDA-ARS Technical Bulletin 1768. 235.Google Scholar
O'Connell, P. E. and Todini, E. 1996. Modeling of rainfall, flow and mass transport in hydrological systems: an overview. J. Hydrol. 175 (1–4):3–16.CrossRefGoogle Scholar
Oriade, C. A. and Forcella, F. 1999. Maximizing efficacy and economics of mechanical weed control in row crops through forecasts of weed emergence. J. Crop Prod. 2:189–205.CrossRefGoogle Scholar
Phillips, D. L., Dolph, J., and Marks, D. 1992. A comparison of geostatistical procedures for spatial analysis of precipitation in mountainous terrain. Agric. Forest Meteorol. 58:19–141.Google Scholar
Plauborg, F., Iversen, B. V., and Laerke, P. E. 2005. In situ comparison of three dielectric soil moisture sensors in drip irrigated sandy soils. Vadose Zone J. 4:1037–1047.CrossRefGoogle Scholar
Pruski, F. F. and Nearing, M. A. 2002. Runoff and soil-loss responses to changes in precipitation: a computer simulation study. J. Soil Wat. Conserv. 57 (1):7–15.Google Scholar
Rawls, W. J., Brakensiek, D. L., and Saxton, K. E. 1982. Estimation of soil water properties. Trans. Am. Soc. Agric. Eng. 25:1316–1320.Google Scholar
Richardson, C. W. 1981. Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour. Res. 17:182–190.CrossRefGoogle Scholar
Richardson, C. W. and Wright, D. A. 1984. WGEN: A Model for Generating Daily Weather Variables. USDA-ARS Publication ARS-8. 83.Google Scholar
Saxton, K. E. and Rawls, W. J. 2006. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci. Soc. Am. J. 70:1569–1578.Google Scholar
Schutte, B. J., Regnier, E. E., Harrison, S. K., Schmoll, J. T., Spokas, K. A., and Forcella, F. 2008. A hydrothermal seedling emergence model for giant ragweed (Ambrosia trifida). Weed Sci. 56:555–560.CrossRefGoogle Scholar
Semenov, M. A., Brooks, R. J., Barrow, E. M., and Richardson, C. W. 1998. Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Clim. Res. 10:95–107.CrossRefGoogle Scholar
Šimůnek, J., van Genuchten, M. Th, and Sejna, M. 2008. Development and applications of the HYDRUS and STANMOD software packages and related codes. Vadose Zone J. 7:587–600.CrossRefGoogle Scholar
Sinden, J., Jones, R., Hester, S., Odom, D., Kalisch, C., James, R., and Cacho, O. 2004. The economic impact of weeds in Australia. CRC for Australian Weed Management Technical Series No. 8.Google Scholar
Soet, M. and Stricker, J. N. M. 2003. Functional behaviour of pedotransfer functions in soil water flow simulation. Hydrol. Proc. 17:1659–1670.CrossRefGoogle Scholar
Spaans, E. J. A. and Baker, J. M. 1991. Calibration of Watermark soil moisture sensors for soil matrix potential and temperature. Plant Soil. 143:213–217.Google Scholar
Spokas, K. and Forcella, F. 2006. Estimating hourly incoming solar radiation from limited meteorological data. Weed Sci. 54:182–189.Google Scholar
Spokas, K., Forcella, F., Archer, D., and Reicosky, D. 2007. SeedChaser: vertical soil tillage distribution model. Comput. Electron. Agric. 57:62–73.CrossRefGoogle Scholar
Stockle, C. O. and Nelson, R. 1999. ClimGen, A Weather Generator Program. Washington State University.Google Scholar
Thomson, S. J. and Armstrong, C. F. 1987. Calibration of the Watermark model 200 soil moisture sensor. Appl. Eng. Agric. 3:186–189.Google Scholar
Thomson, S. J., Yuomos, T., and Wood, K. 1996. Evaluation of calibration equations and application methods for the Watermark granular matrix soil moisture sensor. Appl. Eng. Agric. 12:99–103.Google Scholar
Thornton, P. E., Running, S. W., and White, M. A. 1997. Generating surfaces of daily meteorological variables over large complex terrain. J. Hydrol. 190:214–251.CrossRefGoogle Scholar
Verberg, K., Keating, B. A., Huth, N. I., Ross, P. J., and Catchpoole, V. R. 1996. Evaluation of nitrogen fertiliser management strategies in sugarcane using APSIM-SWIM. Pages 200–202. in Hogarth, J. R., Campbell, D. M., and Garside, J. A. Sugarcane: Research Towards Efficient and Sustainable Production. Brisbane, Australia Wilson, CSIRO Div. of Tropical Crops & Past.Google Scholar
Vitel, Inc 1994. Hydra Soil Moisture Probe User's Manual. Version 1.2. Chantilly, VA Vitel Inc.Google Scholar
Walker, J. P., Willgoose, G. R., and Kalma, J. D. 2001. One-dimensional soil moisture profile retrieval by assimilation of near-surface measurements: a simplified soil moisture model and field application. J. Hydrometeorol. 2:356–373.Google Scholar
Wilks, D. S. and Wilby, R. L. 1999. The weather generation game: a review of stochastic weather models. Prog. Phys. Geogr. 23:329–357.CrossRefGoogle Scholar
Williams, J. R. 1995. The EPIC model. Pages 909–1000. in Singh, V. P. Computer Models of Watershed Hydrology. Highlands Ranch, CO Water Resource Publications.Google Scholar
Willmott, C. J. 1981. On the validation of models. Physical Geography. 2:184–194.Google Scholar
Willmott, C. J. 1982. Some comments on the evaluation of model performance. Bull. Am. Meteor. Soc. 64:1309–1313.Google Scholar
Willmott, C. J. and Matsuura, K. 1995. Smart interpolation of annually averaged air temperature in the United States. J. Appl. Meteorol. 34:2577–2586.Google Scholar
Willmott, C. J. and Matsuura, K. 2005. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim. Res. 30:79–82.Google Scholar
Xiao, W., Yu, Q., Flerchinger, G. N., and Zheng, Y. 2006. Evaluation of SHAW model in simulating energy balance, leaf temperature, and micrometeorological variables within a maize canopy. Agron. J. 98:722–729.CrossRefGoogle Scholar
Yu, C., Warrick, A. W., and Conklin, M. H. 1999. Derived functions of time domain reflectometry for soil moisture measurement. Water Resour. Res. 35:1789–1796.CrossRefGoogle Scholar