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MODELING

Evaluating CROPGRO-Soybean Performance for Use in Climate Impact Studies

^a Dep. of Geogr., Univ. of South Carolina, Columbia, SC 29208
^b Natl. Cent. for Atmos. Res., P.O. Box 3000, Boulder, CO 80303
^c Dep. of Agric. and Biol. Eng., Univ. of Florida, Gainesville, FL 32611
^d USDA-ARS, Coastal Plains Soil, Water, and Plant Res. Cent., 2611 W. Lucas St., Florence, SC 29501
^e Dep. of Biol. and Agric. Eng., Univ. of Georgia, Athens, GA 30602

^* Corresponding author (greg.carbone{at}sc.edu)

Received for publication September 17, 2001.

	ABSTRACT

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Researchers frequently use crop simulation models to estimate the impacts of climate change on agricultural production. While most models used for this purpose have been validated thoroughly at the research plot level, few studies have evaluated them for multiple years and sites and with inputs commonly used in climate impact studies. Here, we examine how well CROPGRO-Soybean performs across space and time using cultivar coefficients provided with the model, estimates of solar radiation, and soil inputs that were estimated from readily available soil surveys. Modeled yield for three maturity groups was compared with that observed from 8 to 23 yr at eight agricultural experiment stations in the southeastern United States. The model was evaluated with respect to its ability to replicate the mean and standard deviation of observed yield. The mean squared deviation (MSD), weighted according to the number of years at each station, was 0.42 (Mg ha^-1)². The model simulated mean yield and the magnitude of interannual yield variability very well. The component of MSD related to the pattern of interannual variability contributed most to MSD. Our results support the use of crop models in studies that require accurate simulation of the temporal mean and variance of yields.

Abbreviations: LCS, lack of correlation weighted by the standard deviations • MSD, mean squared deviation • SB, squared bias • SD, standard deviation • SDSD, squared difference between standard deviations

	INTRODUCTION

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PROCESS-ORIENTED CROP MODELS have been used extensively in climate impact studies (Rosenzweig and Iglesias, 1994; Hoogenboom et al., 1995; Haskett et al., 1997; Mearns et al., 1999; Alexandrov and Hoogenboom, 2000; Guereña et al., 2001) because they simulate crop response to changing climatic conditions. This confidence results from model validation at the scale of research plots. For example, the model used in this study—CROPGRO-Soybean (Hoogenboom et al., 1994; Boote et al., 1998)—has been shown to accurately predict reproductive development and yield (Brisson et al., 1989; Colson et al., 1995; Sau et al., 1999) at various field locations. Because researchers often use these models to investigate climate impacts, the models also should be evaluated in ways that supplement this field-level validation and more closely resemble the means by which inputs are derived for impact studies. This includes simulation during multiple years, at different locations, and with inputs that are not measured directly in the field, but estimated from other published or unpublished data sources. Such inputs include solar radiation, cultivar coefficients, soil wilting point, field capacity, and saturation levels.

Most studies estimating how climate change influences agriculture are conducted at regional or continental scales. At these scales, practical considerations limit generation of the spatially detailed weather, cultivar, soil, and management inputs demanded by many crop models. Therefore, researchers often must characterize regional conditions without access to detailed physical samples (e.g., Curry et al., 1995; Alexandrov and Hoogenboom, 2000). There is a need to evaluate crop models using a methodology similar to that used in many climate impact studies (e.g., Mearns et al., 1999, 2001; Brown et al., 2000; Easterling et al., 2001).

Yield trials from state agricultural experiment stations provide an appropriate data source to examine general model performance during many consecutive years (Mavromatis et al., 2001, 2002). While data limitations preclude intensive model evaluation at the experiment stations, the relatively long time series of yield is particularly relevant to most research on agricultural impacts where climate change scenarios are developed by adjusting observed meteorological time series and preserving interannual variability (Acock and Acock, 1991). Evaluation of the magnitude and pattern of deviations between simulated and observed yield for multiple years and stations can measure, under relatively controlled conditions, how uncertainties inherent in model inputs affect simulated yield estimates (Bouman, 1994; Moen et al., 1994; Nonhebel, 1994; Hansen and Jones, 2000). Moreover, crop model simulation for multiple years allows evaluation of interannual variability, an important quality for climate impact research (Aggarwal, 1995; Mearns et al., 1996, 1997; Kiniry et al., 1997).

	MATERIALS AND METHODS

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CROPGRO-Soybean and Data Inputs
CROPGRO-Soybean v. 3.5 is a process-oriented model that simulates C, water, and N balances for the soybean [Glycine max (L.) Merr.] plant and soil (Hoogenboom et al., 1994; Boote et al., 1998). Model equations express relationships between plant processes (including phenological development, photosynthesis, respiration, biomass growth and partitioning, and soil–plant water balance) and daily temperature, photoperiod, insolation, and water or N stress. Growth is integrated at a daily time step, ultimately predicting biomass, leaf area, seed dry weight, and yield. Cultivar coefficients determine duration within growth stages, response to daylength, maximum growth rates, and seed characteristics. Like many similar crop models, CROPGRO-Soybean does not explicitly treat weeds, diseases, pests, wind damage, or other extreme weather events although efforts have been made to address some of these issues (Batchelor et al., 1993).

The model requires daily weather inputs, soil parameters at various profile depths, and cultural or management details. In this study, direct field measurements from eight agricultural experiment stations in Alabama, Georgia, Louisiana, and South Carolina, USA (Fig. 1) , were used as inputs whenever possible. For example, daily maximum and minimum air temperatures and precipitation were obtained from the cooperative weather stations associated with each experiment station. In contrast, solar radiation was measured at only one station (Tifton, GA) and had to be generated stochastically with WXGEN (Richardson and Nicks, 1990) elsewhere.

View larger version (12K): [in this window] [in a new window]   Fig. 1. Locations of eight agricultural experiment stations used in analysis.

We used agricultural experiment station performance reports for crop and management inputs, including row spacing, plant population, planting date, irrigation and fertilizer application, and cultivar. CROPGRO-Soybean v. 3.5 includes 15 cultivar coefficients that characterize phenology and vegetative and reproductive growth. Published values of these coefficients are available for a variety of individual cultivars as well as generic maturity groups, 000 to X (Tsuji et al., 1994). These latter cultivar coefficients were derived from experiments with several varieties belonging to each particular maturity group. We used published cultivar coefficients for two specific cultivars—Forrest (maturity group V) and Ransom (maturity group VII)—at the three Alabama stations where these varieties were consistently grown during a 20-yr period. At the other stations, we used cultivar coefficients for generic maturity groups V, VI, and VII. We did not tune the coefficients for local conditions using observed data. While this could affect model performance, our goal was to measure how well the model performs using data typically available for a climate impact study.

For all simulations, the CROPGRO model was run with water balance, soil N balance, N fixation, and plant N balance options turned on. We ran the crop model for 4 yr before the first growing season to estimate initial soil water and N amounts. We ran the model in sequence mode through consecutive years to preserve soil moisture values from one season to the next. Simulation length varied according to data availability at each station (Table 1).

These measures allowed us to examine the relative contribution to MSDs between modeled and observed yield (adjusted to account for moisture content). Our analysis included examination of overall model performance at individual stations and for specific varieties or maturity groups. When summarizing for a particular maturity group, we weighted results by the number of seasons of each contributing station. We compared differences between mean observed and mean simulated yield using t tests. The experiment station reports also provided qualitative records of seasonal conditions that help to describe disease and insect problems, lodging, and plant stress that results from nutrient deficiency, water stress, or extreme weather conditions.

	RESULTS

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Maturity Group V
The weighted mean simulated yield for maturity group V across all stations and years was 19% higher than mean observed yield. Simulated yield was significantly higher ( = 0.05) overall and at two individual stations—Fairhope and Marion Junction. The SD was slightly higher for simulated yield than observed yield at each station while the coefficient of variation for simulated and observed yield was about the same (Table 2). The MSD ranged from 0.36 (Mg ha^-1)² at Tifton to 0.96 (Mg ha^-1)² at Marion Junction (Fig. 2) . The LCS component was the largest fraction of MSD at most stations, indicating that the model captured mean yield and the magnitude of interannual yield variation better than the pattern of interannual yield. Closer examination reveals that a few anomalous years disproportionately inflate the MSD. At Crossville, for example, heavy rains in 1972 delayed harvest until 16 Jan. 1973 (Thurlow, 1973). In 1975 and 1979, severe lodging reduced harvestable yield (Fig. 3a ; Thurlow, 1976; Thurlow and Thomason, 1980). At Marion Junction, the model also captured mean yield and the magnitude of variability well but did not reproduce the pattern of interannual variability as well. The results at this station were strongly influenced by large overestimation during 1977, 1986, and 1987 (Fig. 3b) when severe Fe chlorosis reduced yield (Thurlow, 1978; Thurlow and Johnson, 1987, 1988). Such nutrient deficiencies are not simulated by CROPGRO-Soybean; all factors not explicitly accounted for are considered nonlimiting. When these years are eliminated, MSD drops significantly (Table 3).

View larger version (29K): [in this window] [in a new window]   Fig. 2. Components of mean squared deviation for maturity group V and Forrest cultivar. LCS, lack of correlation weighted by the standard deviations; SDSD, squared difference between standard deviations; SB, squared bias.

View larger version (34K): [in this window] [in a new window]   Fig. 3. Time series of simulated and observed yield for Forrest cultivar at (a) Crossville, AL, and (b) Marion Junction, AL.

View larger version (23K): [in this window] [in a new window]   Fig. 4. Components of mean squared deviation for maturity group VI. LCS, lack of correlation weighted by the standard deviations; SDSD, squared difference between standard deviations; SB, squared bias.

View larger version (13K): [in this window] [in a new window]   Fig. 5. Time series of simulated and observed yield for maturity group VI at Jeanerette, LA.

View larger version (33K): [in this window] [in a new window]   Fig. 6. Components of mean squared deviation for maturity group VII and Ransom cultivar. LCS, lack of correlation weighted by the standard deviations; SDSD, squared difference between standard deviations; SB, squared bias.

View larger version (30K): [in this window] [in a new window]   Fig. 7. Time series of simulated and observed yield for (a) maturity group VII at St. Joseph, LA, and (b) Ransom cultivar at Fairhope, AL.

	DISCUSSION

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Our MSD values are slightly higher than those of previous work that used direct solar radiation or soil moisture measurement or adjusted soil parameters or genetic coefficients to minimize observed vs. simulated yield. Of course, these detailed validation studies are required for model improvement. So too is evaluation of crop models across broad regions and over multiple years using inputs that are not directly measured because this is the context within which many climate impact studies use these important tools.

The MSD is sensitive to large individual differences between observed and simulated yield, and our overall results reflect such sensitivity. In most of the years where we found large differences between observed and simulated yield, extreme weather events or other factors (e.g., lodging, pests, soil nutrient deficiencies, or disease) compromised model performance. CROPGRO-Soybean does not account for such factors that reduce observed yield. This leads to overprediction, which inflates the SB component of the MSD. Overestimation at Fairhope, where yield was reduced because of hurricane damage during several years, provides an obvious example. Extreme events also influence the LCS component by affecting the pattern of interannual variability. The example for maturity group VII at St. Joseph demonstrates that while mean yield and the magnitude of interannual variability of simulated and observed yield match well, poor model performance during only 1 or 2 yr leads to a higher LCS, inflating MSD (Fig. 7a).

CROPGRO-Soybean performed well under a wide range of environmental conditions, despite the use of several inputs that were not measured directly in the field. The pattern of interannual variability, which was largely influenced by poor model simulation during individual years, contributed most significantly to MSDs between simulated and observed yield. In many of the individual years when the model overestimated yield, observed values were influenced by pests, lodging, soil nutrient deficiencies, or extreme weather events. Because most crop models currently do not consider these effects completely, it is unreasonable to expect that the models will perform well in every individual year. When such years are eliminated, MSD drops significantly.

Model deviations also should be viewed within the context of specific applications. Changes in mean yield and the magnitude of yield variance often serve as inputs to agricultural economics models (Lambert et al., 1995; McCarl et al., 2000) used in long-term climate change studies. Some have argued that accurate prediction of the range of interannual yield variability is more important than estimating absolute yield values in an individual year because year-to-year variability has greater potential economic impact (Moen et al., 1994). While Haskett et al. (1995) found poor performance during individual years, they argued that an ability to successfully simulate soybean mean yield and long-term variability justifies model use for climate change studies. The results presented here suggest that CROPGRO-Soybean meets this criterion.

	ACKNOWLEDGMENTS

 
The authors gratefully acknowledge Larry McDaniel and Jennifer Rainman for data preparation and graphics assistance and Dr. Gerrit Hoogenboom, Dr. William Payne, Karen Beidel, and three anonymous reviewers for their comments on the first draft of this paper. This research was supported by the U.S. Environmental Protection Agency, NCERQA (Grant no. R824997-01-C); the National Aeronautics and Space Administration, MTPE (Grant no. OA99073, W-19, 080); and the U.S. Department of Agriculture, NRICGP (98-35106-6837).

	REFERENCES

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• Acock, B., and M.C. Acock. 1991. Potential for using long-term field research data to develop and validate crop simulators. Agron. J. 83:56–61.[Abstract/Free Full Text]
• Aggarwal, P.K. 1995. Uncertainties in crop, soil, and weather inputs used in growth models: Implications for simulated outputs and their applications. Agric. Syst. 48:361–384.
• Alexandrov, V.A., and G. Hoogenboom. 2000. Vulnerability and adaptation assessments of agricultural crops under climate change in the southeastern USA. Theor. Appl. Climatol. 67:45–63.
• Batchelor, W.D., J.W. Jones, K.J. Boote, and H.O. Pinnschmidt. 1993. Extending the use of crop models to study pest damage. Trans. ASAE 36:551–558.
• Baumer, O., P. Kenyon, and J. Bettis. 1994. Prediction of soil properties. Map Unit Use Files (MUUF). User's manual version 2.14. Nat. Resour. Conserv. Serv., Wetland Sci. Inst., Laurel, MD.
• Baumer, O.W., and J.W. Rice. 1988. Methods to predict soil input data for DRAINMOD. ASAE Paper 88–2564. Am. Soc. of Agric. Eng., St. Joseph, MI.
• Boote, K.J., J.W. Jones, and N.B. Pickering. 1998. The CROPGRO model for grain legumes. p. 99–128. In G. Tsuji, G. Hoogenboom, and P.K. Thornton (ed.) Understanding options for agricultural production. Kluwer Academic Publ., Dordrecht, the Netherlands.
• Bouman, B.A.M. 1994. A framework to deal with uncertainty in soil and management parameters in crop yield simulation: A case study for rice. Agric. Syst. 46:1–17.
• Brisson, N., S. Bona, and A. Bouniols. 1989. SOYGRO, un modèle de simulation de la culture du soja: Adaptation à des variétés dans le sud de l'Europe et validation. Agronomie (Paris) 9:27–37.
• Brown, R.A., N.J. Rosenberg, C.J. Hays, W.E. Easterling, and L.O. Mearns. 2000. Potential production and environmental effects of switchgrass and traditional crops under current and greenhouse-altered climate in the central United States: A simulation study. Agric. Ecosyst. Environ. 78:31–47.
• Colson, J., A. Bouniols, and J.W. Jones. 1995. Soybean reproductive development: Adapting a model for European cultivars. Agron. J. 87:1129–1139.[Abstract/Free Full Text]
• Curry, R.B., J.W. Jones, K.J. Boote, R.M. Peart, L.H. Allen, Jr., and N.B. Pickering. 1995. Responses of soybean to predicted climate change in the USA. p. 163–182. In C. Rosenzweig, L. H. Allen, Jr., L. A. Harper, S. E. Hollinger, and J. W. Jones, (ed.) Climate change and agriculture: Analysis of potential international impacts. ASA Spec. Publ. 59. ASA, CSSA, and SSSA, Madison, WI.
• Easterling, W.E., L.O. Mearns, C.J. Hays, and D. Marx. 2001. Comparison of agricultural impacts of climate change calculated from high and low resolution climate change scenarios: Part II. Accounting for adaptation and CO₂ direct effects. Clim. Change 51:173–197.
• Egli, D.B., and W. Bruening. 1992. Planting date and soybean yield: Evaluation of environmental effects with a crop simulation model: SOYGRO. Agric. For. Meteorol. 62:19–29.
• Gijsman, A.J., S.S. Jagtap, and J.W. Jones. 2002. Wading through a swamp of complete confusion: How to choose a method for estimating soil water retention parameters for crop models. Eur. J. Agron. 18(1–2):75–105.
• Guereña, A., M. Ruiz-Ramos, C.H. Díaz-Ambrona, J.R. Conde, and M.I. Mínguez. 2001. Assessment of climate change and agriculture in Spain using climate models. Agron. J. 93:237–249.[Abstract/Free Full Text]
• Hansen, J.W., and J.W. Jones. 2000. Scaling-up crop models for climate variability applications. Agric. Syst. 65:43–72.
• Haskett, J.D., Y.A. Pachepsky, and B. Acock. 1995. Estimation of soybean yields at county and state levels using GLYCIM: A case study for Iowa. Agron. J. 87:926–931.[Abstract/Free Full Text]
• Haskett, J.D., A. Yakov, Y.A. Pachepsky, and B. Acock. 1997. Increase of CO₂ and climate change effects in Iowa soybean yield, simulated using GLYCIM. Agron. J. 89:167–176.[Abstract/Free Full Text]
• Hoogenboom, G. 2000. Contribution of agrometeorology to the simulation of crop production and its applications. Agric. For. Meteorol. 103:137–157.
• Hoogenboom, G., J.W. Jones, P.W. Wilkens, W.D. Batchelor, W.T. Bowen, L.A. Hunt, N.B. Pickering, U. Singh, D.C. Godwin, B. Baer, K.J. Boote, J.T. Ritchie, and J.W. White. 1994. Crop models. p. 95–244. In G.Y. Tsuji, G. Uehara, and S. Balas (ed.) DSSAT v3. Univ. of Hawaii, Honolulu.
• Hoogenboom, G., G.Y. Tsuji, N.B. Pickering, R.B. Curry, J.W. Jones, U. Singh, and D.C. Godwin. 1995. Decision support system to study climate change impacts on crop production. p. 52–75. In C. Rosenzweig, L.H. Allen, Jr., L.A. Harper, S.E. Hollinger, and J.W. Jones (ed.) Climate change and agriculture: Analysis of potential international impacts. ASA Spec. Publ. 59. ASA, CSSA, and SSSA, Madison, WI.
• Jones, J.W., and J.R. Ritchie. 1990. Crop growth models. p. 63–89. In G.J. Hoffman, T.A. Howell, and K.H. Solomon (ed.) Management of farm irrigation systems. Am. Soc. of Agric. Eng., St. Joseph, MI.
• Kiniry, J.R., J.R. Williams, R.L. Vanderlip, J.D. Atwood, D.C. Reicosky, J Mulliken, W.J. Cox, H.J. Mascagni, S.E. Hollinger, and W.J. Wiebold. 1997. Evaluation of two maize models for nine U.S. locations. Agron. J. 89:421–426.[Abstract/Free Full Text]
• Kobayashi, K., and M.U. Salam. 2000. Comparing simulated and measured values using mean squared deviation and its components. Agron. J. 92:345–352.[Abstract/Free Full Text]
• Lambert, D.K., B.A. McCarl, Q. He, S. Kaylen, W. Rosenthal, C.C. Chang, and W.I. Nayda. 1995. Uncertain yields in sectoral welfare analysis: An application to global warming. J. Agric. Appl. Econ. 27:423–436.
• Mavromatis, T., K.J. Boote, J.W. Jones, A. Irmak, D. Shinde, and G. Hoogenboom. 2001. Developing genetic coefficients for crop simulation models using data from crop performance trials. Crop Sci. 41:40–51.[Abstract/Free Full Text]
• Mavromatis, T., K.J. Boote, J.W. Jones, G.G. Wilkerson, and G. Hoogenboom. 2002. Repeatability of model genetic coefficients derived from soybean performance trials across different states. Crop Sci. 42:76–89.[Abstract/Free Full Text]
• McCarl, B.A., C.C. Chang, J.D. Atwood, and W.I. Nayda. 2000. Documentation of ASM: The U.S. Agricultural Sector Model [Online]. Available at http://agecon.tamu.edu/faculty/mccarl/asm.html (verified 17 Jan. 2003). Bruce McCarl, College Station, TX.
• Mearns, L.O., W.E. Easterling, C.J. Hays, and D. Marx. 2001. Comparison of agricultural impacts of climate change calculated from high and low resolution climate change scenarios: Part I. The uncertainty due to spatial scale. Clim. Change 51:131–172.
• Mearns, L.O., T. Mavromatis, E. Tsvetsinskaya, C. Hays, and W. Easterling. 1999. Comparative responses of EPIC and CERES crop models to high and low resolution climate change scenarios. J. Geophys. Res. 104(D):6623–6646.
• Mearns, L.O., C. Rosenzweig, and R. Goldberg. 1996. The effect of changes in daily and interannual climatic variability on CERES-Wheat: A sensitivity study. Clim. Change 32:257–292.
• Mearns, L.O., C. Rosenzweig, and R. Goldberg. 1997. Mean and variance change in climate scenarios: Methods, agricultural applications, and measures of uncertainty. Clim. Change 35:367–396.
• Moen, T.N., H.M. Kaiser, and S.J. Riha. 1994. Regional yield estimation using a crop simulation model: Concepts, methods, and validation. Agric. Syst. 46:79–92.
• Natural Resources Conservation Service. 1988. Map Unit User File—a DOS program to develop soils files to use in conjunction with DRAINMOD. Available at ftp://ftp.wcc.nrcs.usda.gov/water_mgt/muuf (verified 17 Jan. 2003). USDA-NRCS, Natl. Water and Clim. Cent., Beltsville, MD.
• Nonhebel, S. 1994. The effects of average instead of daily weather data in crop growth simulation models. Agric. Syst. 44:377–396.
• Richardson, C.W., and A.D. Nicks. 1990. Weather generator description. p. 93–104. In A.N. Sharpley and J.R. Williams (ed.) EPIC—Erosion/Productivity Impact Calculator: I. Model documentation. Tech. Bull. 1768. USDA-ARS, Washington, DC.
• Rosenzweig, C., and A. Iglesias (ed.). 1994. Implications of climate change for international agriculture: Crop modeling study. USEPA 230–8-94–003. USEPA, Washington, DC.
• Sau, F., K.J. Boote, and B. Ruiz-Nogueira. 1999. Evaluation and improvement of CROPGRO-soybean model for a cool environment in Galicia, northwest Spain. Field Crops Res. 61(3):273–291.
• Soil Survey Division, Natural Resources Conservation Service, U.S. Department of Agriculture. 2001. Official soil series descriptions [Online]. Available at http://soils.usda.gov/ (verified 17 Jan. 2003). USDA-NRCS, Soil Survey Div., Washington, DC.
• Thurlow, D.L. 1973. Alabama soybean variety tests, 1972. Agron. and Soils Dep. Ser. 10. Agric. Exp. Stn., Auburn, AL.
• Thurlow, D.L. 1976. Alabama soybean variety tests, 1975. Agron. and Soils Dep. Ser. 29. Agric. Exp. Stn., Auburn, AL.
• Thurlow, D.L. 1978. Alabama soybean variety tests, 1977. Agron. and Soils Dep. Ser. 49. Agric. Exp. Stn., Auburn, AL.
• Thurlow, D.L., and W.C. Johnson. 1987. Performance of soybean varieties in Alabama, 1986. Agron. and Soils Dep. Ser. 116. Agric. Exp. Stn., Auburn, AL.
• Thurlow, D.L., and W.C. Johnson. 1988. Performance of soybean varieties in Alabama, 1987. Agron. and Soils Dep. Ser. 123. Agric. Exp. Stn., Auburn, AL.
• Thurlow, D.L., and G.E. Thomason, Jr. 1980. Alabama soybean variety tests, 1979. Agron. and Soils Dep. Ser. 56. Agric. Exp. Stn., Auburn, AL.
• Tsuji, G., G. Uehara, and S. Balas (ed.). 1994. DSSAT version 3. User's guide. Int. Benchmark Sites Network for Agrotechnol. Transfer, Dep. of Agron. and Soil Sci., College of Trop. Agric. and Human Resour., Univ. of Hawaii, Honolulu.
• [USDA-NRCS] U.S. Department of Agriculture Natural Resources Conservation Service. 2001. National STATSGO database [Online]. Available at http://www.ftw.nrcs.usda.gov/stat_data.html (accessed 31 May 2002; verified 17 Jan. 2003). USDA-NRCS, Soil Survey Div., Washington, DC.
• van Genuchten, M.T., and D.R. Nielsen. 1985. On describing and predicting the hydraulic properties of unsaturated soils. Ann. Geophys. 3:615–628.
• Willmott, C.J. 1982. Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. 63:1310–1313.

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