This chapter examines, largely from a theoretical standpoint, strategies for improvements in crop water and nitrogen (N) using multiple cropping. The combinations of possible multiple-cropping systems, each with its unique problems and advantages, are too numerous to discuss, but a general definition of systems is given. Similarly, the definitions of water and N-use efficiency vary greatly depending on the purposes for examining efficiencies. The chapter focuses on water and N-use efficiencies obtained through multiple cropping that are above and beyond those that may be obtained within a single crop. Since water is often the most limiting resource, any improvement in water resource-use efficiency (RUE) due to multiple cropping may also lead to enhanced use of other resources. When water and N are underutilized or lost by the practice of one crop per year, the added RUE afforded by multiple cropping suggests that they should be considered in the analysis.

Increased crop production and expansion of irrigated acreage in the southeastern USA have increased agricultural water use during the past two decades. To optimize irrigation water use, it is important to know when to irrigate and how much water should be applied. The objectives of this study were (1) to evaluate the Cropping System Model (CSM)-CERES-Maize model with measured data of the amount of water required for supplemental irrigation and (2) to apply the CSM-CERES-Maize model for estimating irrigation water use for maize in the southeastern USA. The CSM-CERES-Maize model was evaluated for 2000-2004 for five counties that represent the dominant maize production regions in South Georgia. For each county, historical daily weather data, three representative soil profiles, and specific crop management recommendations were used as input for the model. The simulated results were then compared with observed data obtained during the same period. The amount of water required for irrigation for each growing season was simulated for 58 years using historical weather data from 1950 to 2007 for 88 selected counties that corresponded to the most important agricultural production region in Georgia. Both monthly and annual water demand for maize was determined for each county. The total seasonal amount of water required for irrigation across counties and years ranged from 136 to 281 mm, with an average of 227 mm. The irrigation requirements among months varied from 10 to 79 mm. with the highest amount required for May. The results from the evaluation showed that the model was able to simulate the amount of water required for maize irrigation in good agreement with the observed data. This demonstrated the potential application of the CSM-CERES-Maize model as a tool for estimating water demand for irrigation. The estimated water requirements for supplemental irrigation can be used by both policy makers and local farmers for planning the amount of water required for supplemental irrigation as well as for improvements in irrigation management for water conservation. (C) 2012 Elsevier B.V. All rights reserved.

Conservation tillage, particularly no-till, has a significant role to play toward achieving agricultural water conservation goals envisaged in Georgia’s Comprehensive Statewide Water Management Planning Act of 2004. We base this on scientific evidence from across the country and our own research showing that conservation tillage allows substantially more of rain and/or irrigation water to infiltrate/percolate into the soil compared to conventional tillage methods, thus reducing much runoff waste. In one study spanning May 1, 1997 to May 5, 1998 near Watkinsville, GA, we found an extra 6.93 inches of rain water infiltrated into the soil profile in a no-till cotton/rye system compared to conventional tillage. This represents 14% of the average annual rainfall and is equivalent to more than 188 billion gallons of water from one million acres of cropland, which is about a third of Georgia’s harvested cropland. Annual irrigation use in Georgia fluctuates between 100 and 300 billion gallons. Additionally, conservation tillage reduces sediment that alters critical habitat and stream flow, and reduces nonpoint source contaminants that require additional assimilative capacity in those streams. While the current agricultural water conservation plan rightly targets potential waste in irrigated agriculture through retrofitting irrigation system components, conservation tillage offers water conservation both in irrigated and non-irrigated agriculture. For this potential to materialize, aggressive leadership that provides both political will and appropriate resources is needed across all government agencies and non-government organizations (NGOs) involved in natural resource policy formulation, research, education, extension, and outreach.

In Georgia and many other southeastern states in the USA, the amount of water used by agriculture for irrigation is largely unknown due to the lack of reporting requirements. Recent droughts and a water dispute with the neighboring states that include Alabama and Florida have highlighted the need for an accurate estimate of water use by agriculture. The goal of this study was to evaluate the use of a crop simulation model combined with kriging for estimating the spatial distribution of the monthly irrigation water use for cotton in the Coastal Plain region of Georgia. Farmers’ monthly irrigation applications for cotton during the 2002 and 2003 growing seasons were obtained from selected sites of the Agricultural Water Pumping program. We selected 80 fields for 2002 and 51 fields for 2003. For each of these fields, we used the Cropping System Model-CROPGRO-Cotton to simulate farmers’ irrigation applications. Ordinary kriging was used to estimate the spatial distribution of monthly total irrigation in the region. We then compared the spatial and temporal distribution of irrigation amounts predicted by the Cropping System Model-CROPGRO-Cotton with the amount of water that the farmers actually applied. The Cropping System Model-CROPGRO-Cotton simulated the temporal pattern of irrigation applications very well during the growing season. The root mean square error (RMSE) between observed and simulated total irrigation for different months ranged from 5 to 23mm in 2002 and from 2 to 14mm in 2003. The RMSE values were generally higher in 2002 when the irrigation applications across the region were more variable when compared with 2003. Consequently, a better agreement on the spatial distribution of monthly total irrigation for the observed and simulated was obtained for 2003 than for 2002.

An understanding of water needs in agriculture is a critical input in resolving the water resource issues that confront many southeastern and other US states. The objective of this study was to evaluate on-farm irrigation applications for three major crops grown in Georgia, USA using the Environmental Policy Integrated Climate (EPIC) model. For cotton, 16, 58, and 75 farmers' fields in 2000, 2001, and 2002, respectively, were selected from among the Agricultural Water Pumping (AWP) program sites across the state of Georgia. For maize, 9, 20, and 28 fields were selected in 2000, 2001, and 2002, respectively, and for peanut, 18, 51, and 54 fields were selected in 2000, 2001, and 2002, respectively. The majority of these fields were located in the southwest region of Georgia, where traditional row-crop agriculture is most dominant. We compared the simulated irrigation requirements with the amount of water that the farmers actually applied during the 2000, 2001, and 2002 growing seasons. For cotton and peanut, the means of farmer-applied irrigation amounts and simulated irrigation requirements agreed very well, with similar values for root mean squared deviation (RMSD) of the two crops. For maize, good agreement between simulated and farmer-applied irrigation amounts were found only in 2001. Farmers applied more water to their maize crop when compared to simulated irrigation requirements, especially when rainfall was very low and potential evapotranspiration was high during the 2000 and 2002 growing seasons. The component of the mean squared deviation (MSD = RMSD2) related to the pattern of variability in seasonal irrigation applications contributed most to MSD. Accurate estimates of the mean and the magnitude of variability in seasonal irrigation applications could be very useful for the estimation of overall water use by agriculture in Georgia and other southeastern states. This study showed that the EPIC model would be an adequate tool for this purpose; potential users could include policy makers, planners and regulators, including the Georgia Department of Natural Resources (DNR).

A new, inexpensive irrigation scheduling device called the UGA EASY Pan is introduced for application to sprinklerirrigated row-crop production systems in humid areas. The device uses a washtub and a float system to determine waterloss from the pan as it relates to crop water loss. The device can be set to represent different crops and soil water holdingconditions. An indicator arm allows reading of the device from the edge of an irrigated field. Initial test results indicate reasonablecomparison with other irrigation scheduling methods for cotton and peanut on loamy sand soils in south Georgia. Wateruse on peanut as recommended by the UGA EASY Pan with a 50-mm mesh screen and a setting of 127 mm on the float rodresulted in water use within 5 mm of the total seasonal water use based on the soil water content scheduling method (or control)with similar yields. Using the UGA EASY Pan on cotton with standard window screen and a setting of 127 mm on thefloat rod resulted in total water application of about 150 mm which was within 5 mm of the application amount based on thesoil water content (control) scheduling method. Average yields from the replicated UGA EASY Pan plots were within 0.1 T/haof the yields associated with the control irrigation scheduling method.

An understanding of water needs in agriculture is a critical input in resolving the water resource issues that confront many southeastern states. Unfortunately, how much water is required and how much water is actually being used for irrigation in Georgia is primarily estimated and largely unknown. The objective of this study was to evaluate the performance of the Environmental Policy Integrated Climate (EPIC) model in simulating crop yield and irrigation demand for three major crops in Georgia. Model evaluation is necessary to provide credibility in applying the model for simulating water use by agriculture. Seasonal yield and irrigation data for the 1990 through 2001 crop variety trials conducted at five agricultural experiment stations were used to evaluate simulation of yield and irrigation amount. The root mean squared deviation (RMSD) for yield was 0.29 t/ha for cotton, 0.39 t/ha for soybean, and 1.02 t/ha for peanut. The RMSD for peanut was large because the model tended to underestimate high yields and was not as sensitive to the factors responsible for the year-to-year variability of peanut yield. The RMSD for total amount of irrigation was 75 mm for cotton, 83 mm for soybean, and 87 mm for peanut. The model simulated the mean irrigation amount and the magnitude of annual variability very well. The component of mean squared deviation (MSD = RMSD2) related to the pattern of annual variability in irrigation amount contributed most to MSD. Overall, the results showed that the EPIC model can be a useful tool for simulating crop yield and irrigation demand at a field level. Future efforts will focus on using the model for regional estimation of water use for irrigation in Georgia and other southeastern states.

The purpose of this study was to compare two commonly used runoff experimental methods, which have different scales, on measurements of runoff and associated fenamiphos and metabolite losses over a 2-year period. Methods used were 15 m wide by 43 m long (645 m(2)) mesoplots and 1.8 m wide by 3 in long (5.4 m(2)) microplots, under simulated rainfall (25 mm h(-1) for 2 h) at 1, 14, and 28 d after fenamiphos application. Mesoplots and microplots were established parallel to a 3% slope on a Tifton loamy sand (Plinthic Kandiudult). All plots were planted to corn (Zea mays L.). Target application rate for fenamiphos was 6.7 kg ha(-1). Runoff totals and maximum rates or meso- and microplots were similar with approximately 25% of the rainfall running off mesoplots and approximately 28% running off microplots. Runoff totals and maximum rates from meso- and microplots were each positively correlated (R-2 = 0.89). In both years, fenamiphos lost in runoff decreased with each rainfall event (1, 14, and 28 d after application). The majority of fenamiphos lost in runoff was in the fenamiphos sulfoxide form. Fenamiphos sulfoxide lost over both years from mesoplots ranged from 51% to 93% of the total fenamiphos lost, and loss from microplots ranged from 47% to 100% of the total fenamiphos lost. Runoff from meso- and microplots I d after fenamiphos application, a "reasonable worst-case" event, had the greatest fenamiphos losses among events. Total losses of fenamiphos or this event averaged 1.2% (CV = 26%) of applied amount for mesoplots and 1.3% (CV = 47%) of applied amount for microplots. Maximum (seasonal) fenamiphos losses for meso- and microplots were 1.4% of applied for mesoplots and 2.6% of applied for microplots. A positive correlation was obtained between microplots and mesoplots for total losses of fenamiphos + metabolites (R-2 = 0.88), fenamiphos parent (R-2 = 0.89), and fenamiphos sulfoxide (R-2 = 0.81). Relatively poor agreement was found for relatively small losses of fenamiphos sulfone between plot types (R-2 = 0.34). Microplots and mesoplots yielded statistically similar results in terms of runoff and fenamiphos losses; thus, microplot results can be extrapolated tip to larger mesoplot areas under these conditions. This has implications for field-scale management and watershed assessment in the Coastal Plain region of the southeast U.S. in that microplot and rainfall simulation results could be useful as statistically valid input datasets to estimate runoff and associated fenamiphos losses from larger areas.

Fresh water resources in the world are limited and, often, disputes occur on how to share them. In many regions, agricultural water use is significant but poorly documented. In order to contribute to solutions for water disputes involving such regions, methodologies need to be developed for regional water use estimation. In this paper we present a case study of Georgia (USA) which is locked in a water dispute with its neighboring states—Alabama and Florida. Agricultural water use in Georgia was essentially unknown because of no reporting requirement. Using a geographic information system and geospatial techniques, the depths of irrigation for cotton, peanut, and maize are estimated for the Flint, Central, and Coastal water zones of Georgia for 2000–2002. The geospatial techniques included the Inverse Distance Weighting, Global Polynomial, Local Polynomial, Radial Basis Function, Ordinary Kriging, and Universal Kriging. The volume of irrigation for these crops was estimated for 2000 and 2001. On the basis of root mean squared error, the Radial Basis Function technique was found to be the most successful one, followed by the Local Polynomial technique. The study of variograms revealed that the depth of irrigation at a site was influenced by its neighboring sites within a radius of about 40km in the case of cotton, and within about 70km in the case of peanut. No such influence could be detected for maize. The total volume of irrigation was highest for the Flint zone (564.2Mm3), followed by the Central zone (291.9Mm3) and the Coastal zone (94.1Mm3) for 2000. For 2001, the irrigation volume declined by 40% for the Flint zone, 32% for the Central zone, and 16% for the Coastal zone. The estimates presented in this study can be improved by including more representative sampling sites if possible, by studying the patterns of irrigated lands in Georgia, and by using satellite data for estimating irrigated area for individual crops.

The states of Alabama, Florida and Georgia dispute the apportioning of water from rivers that originate in Georgia and flow through the other two states. Florida and Alabama often claim that Georgia uses more than its fair share of water. In order to address such a dispute, an estimation of the total amount of water used for irrigation by different crops is required. Current estimates of irrigated areas are subject to errors because they are based entirely on survey questionnaires. In this paper, the potential of Advanced Very High Resolution Radiometer (AVHRR) on-board the National Oceanic Space Administration (NOAA) satellites is examined for estimating irrigated area. Two indices, a widely used Normalized Difference Vegetation Index (NDVI) and a newer Vegetation Health Index (VHI), were regressed against irrigated area for 1986, 1989, 1992, 1995 and 2000 for selected regions in Georgia (Baker and Mitchell counties, and Seminole and Decatur counties). The average VHI during a period from the third week of February to the end of September was better related to irrigated area than the corresponding NDVI; R 2 was above 0.80 as opposed to 0.49. It is concluded that the VHI, derived from three-channel AVHRR data, can be used to estimate irrigated area. By multiplying irrigated area with the application rate, the volume of irrigation used in a state can be determined, which can contribute to the solution of the water dispute.

The efficiency of water use from irrigation systems is influenced by temperature, wind,humidity, and solar radiation. Accepted values for water loss efficiency are the results ofyears of tests; however, most of these values do not take areas of high relative humidityinto account. The actual losses of water under different conditions are a critical componentof proposed cost share programs for water conservation with agricultural irrigation. Thepotential benefits of these different sprinkler packages require real world results underactual field conditions. This information can be passed on to farmers so they can obtainbetter use of the water they are pumping. Since center pivot irrigation represents a large percentage of the irrigation systems in thestate of Georgia [over 344 000 ha (850,000 ac)], the potential water conservation benefitsfrom changing to more efficient sprinkler packages is one aspect of the conservationinitiative. The main objective of this research was to determine and compare water losses incenter pivot systems due to the evaporation and wind interference. In order to determine anappropriate location along the center pivot boom where the tests should be run, uniformitytests were developed and conducted for two months. After this process, the loss tests wereconducted during different times of the day in varying weather conditions. This paper includes the procedures used and the initial results from the first season of theloss evaluations. Results show the variability on the average water catch among sprinklerpackages as well as the difference of average collections for atmospheric conditions. Initialresults indicate a 10% increase in losses when the relative humidity is low (40 to 70%) ascompared to a relative humidity between 71 and 99%. Using spray nozzles on top of thepivot boom resulted in the lowest losses within the relative humidity tests, followed byspray nozzles on drops (an additional 6% loss), high angle impact sprinklers (additional16% loss) and low angle impact sprinklers (additional 22% loss). Additional data isrequired to determine if the added cost for drops with spray nozzles on center pivotirrigation systems are justified for water conservation in humid areas.

Water Resources ResearchVolume 37, Issue 3 p. 853-855 CommentariesFree Access Reply [to “Comment on ‘Influence of three-parameter conversion methods between van Genuchten and Brooks-Corey Functions on soil hydraulic properties and water-balance predictions’ by Qingli Ma et al.”] Qingli Ma, Qingli MaSearch for more papers by this authorJames E. Hook, James E. HookSearch for more papers by this authorLaj R. Ahuja, Laj R. AhujaSearch for more papers by this author Qingli Ma, Qingli MaSearch for more papers by this authorJames E. Hook, James E. HookSearch for more papers by this authorLaj R. Ahuja, Laj R. AhujaSearch for more papers by this author First published: 01 March 2001 https://doi.org/10.1029/2000WR900347Citations: 2AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. References Ahuja, L. R., D. G. DeCoursey, B. B. Barnes, K. W. Rojas, Characteristics of macropore transport studied with the ARS root zone water quality model, Trans. ASAE, 36, 369–380, 1993. Brooks, R. H., A. T. Corey, Hydraulic properties of porous media, Hydrol. pap., 3, Colo. State Univ., Fort Collins, 1964. Lenhard, R. J., J. C. Parker, S. Mishra, On the correspondence between Brooks-Corey and van Genuchten models, J. Irrig. Drain. Eng., 115, 744–751, 1989. Ma, Q. L., J. E. Hook, L. R. Ahuja, Influence of three-parameter conversion methods between van Genuchten and Brooks-Corey functions on soil hydraulic properties and water balance predictions, Water Resour. Res., 35, 2571–2578, 1999. Millington, R. J., J. P. Quirk, Permeability of porous solids, Trans. Faraday Soc., 57, 1200–1206, 1961. Morel-Seytoux, H. J., Comment on “Influence of three-parameter conversion methods between van Genuchten and Brooks-Corey functions on soil hydraulic properties and water balance predictions” by Qingli Ma et al.,Water Resour. Res., 3. Morel-Seytoux, H. J., P. D. Meyer, M. Nachabe, J. Touma, M. T. vanGenuchten, R. J. Lenhard, Parameter equivalence for Brooks-Corey and van Genuchten soil characteristics: Preserving the effective capillary drive, Water Resour. Res., 32, 1251–1258, 1996. Mualem, Y., A new model for predicting the hydraulic conductivity of unsaturated porous-media, Water Resour. Res., 12, 513–522, 1976. vanGenuchten, M. T., A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44, 892–898, 1980. Citing Literature Volume37, Issue3March 2001Pages 853-855 ReferencesRelatedInformation

Abstract Peanuts become contaminated with aflatoxins when subjected to prolonged periods of heat and drought stress. The effect of drought tolerance on aflatoxin contamination is not known. The objectives of this research were to evaluate preharvest aflatoxin contamination in peanut genotypes known to have drought tolerance and to determine the correlation of drought tolerance characteristics with aflatoxin contamination. Twenty genotypes with different levels of drought tolerance were grown in Yuma, AZ (a desert environment) and under rain-protected shelters in Tifton, GA. Two drought-tolerant genotypes (PI 145681 and Tifton 8) and an intolerant genotype (PI 196754) were selected for further examination in a second experiment with two planting dates in 1997 at Tifton. Drought and heat stress conditions were imposed for the 40 d preceding harvest. The drought-intolerant genotype had greater preharvest aflatoxin contamination than Florunner (the check cultivar) in the tests conducted in 1997. Both drought-tolerant genotypes had less preharvest aflatoxin contamination than Florunner in these tests. Significant positive correlations were observed between aflatoxin contamination and leaf temperature and between aflatoxin contamination and visual stress ratings. Leaf temperature and visual stress ratings are less variable and less expensive to measure than aflatoxin contamination. Leaf temperature and visual stress ratings maybe useful in indirectly selecting for reduced aflatoxin contamination in breeding populations.

The Brooks‐Corey functions are commonly used in hydrologic models, with parameters obtained by fitting the functions directly to measured soil water retention data or by conversion methods from the van Genuchten functions which are continuous across the domain of matric suctions. Problems in fitting the BC functions directly to the retention data motivated use of the conversion methods. However, differences in converted parameters could significantly influence model predictions. We compared the direct fitting method and the conversion methods of Lenhard et al., Morel‐Seytoux et al., and van Genuchten using measured water retention data during drainage and determined the influence of these methods on hydrological predictions when the converted parameters were used in the root zone water quality model. The conversion methods had significant influence on predictions of water retention, hydraulic conductivity, runoff, and evapotranspiration, with the observed level of significance (p ≤ 0.006) much lower than the test level of significance (α = 0.05). The method of Morel‐Seytoux et al. inadequately described measured water retention data (p=0.027), whereas the other two methods adequately described the data at relatively high suctions (p ≥ 0.687), deviations occurred around the air‐entry suction. The method of Lenhard et al. best reproduced the characteristics of the Brooks‐Corey functions (p ≥ 0.31) and could be used to obtain the Brooks‐Corey parameters simply and reproducibly.

Frequent pesticide applications to gulf courses causes concern that surface water may become contaminated, We hypothesized that runoff potential of these pesticides could be predicted by the recently developed Opus model. We conducted a 3-yr field study measuring surface runoff of water and dimethylamine salts of 2,4-D [(2,4-dichlorophenoxy) acetic acid], dicamba (3,6-dichloro-2-methylphenoxy-benzoic acid), and mecoprop [(+/-)-2-(4-chloro-2-methylphenoxy)-propanoic acid]. Twelve 7.4 m by 3.7 m plots of 'Tifway 419' bermudagrass (Cynodon dactylon (L.) Pers. x C. transvaalensis Burtt Davy) were managed as a golf course fairway. Simulated rainfall was applied at an average intensity of 29 mm h(-1) 1 d before and 1, 2, 4, and 8 d after pesticide application for 0.92, 1.75, 1.75, 0.92, and 0.92 h, respectively. Average annual runoff loss was 9.13, 15.41, and 10.82% of applied 2,4-D, dicamba, and mecoprop, respectively. Both mass and concentration of pesticide runoff decreased rapidly, with the first posttreatment event runoff averaging 74.5, 71.7, and 73.0% of the total runoff of 2,4-D, dicamba, and mecoprop, respectively. The Opus model adequately simulated runoff [R(2) = 0.897 and normalized root mean square error (NRMSE) = 24.6%], The 2,4-D in runoff was better simulated by complete-kinetic sorption (R(2) = 0.876, NRMSE = 60.2%) than by equilibrium sorption (R(2) = 0.848, NRMSE = 68.2%). Opus did not accurately simulate 2,4-D over all runoff events, but simulated 2,4-D in the first posttreatment runoff within a factor of 2 of those measured.

A rainfall simulator was used to apply 5 cm of rainfall in 2 hours to two replicate 624 m 2 plots at six times during each of the growing seasons of 1992 and 1993. Because the simulator generated reproducible and time‐invariant rainfall intensities, the resulting 24 hydrographs reproducibly reveal the effects of tractor wheel compaction, tillage, soil reconsolidation, surface sealing, and corn canopy development. A time series data set including weather, crop development, soils properties, evapotranspiration, and antecedent soil water is available. These data should provide hydrologie modelers, particularly those interested in modeling runoff with time resolutions of <1 day, with a useful validation data set.

A three-year field study was conducted using twelve 7.4 x 3.7 m plots and simulated rainfall to investigate pesticide run-off following application to a golf course fairway. The plots were sprigged with 'Tifway 419' bermudagrass (Cynodon dactylon x C transvaalensis). The dimethylamine salt of 2,4-D [(2,4-dichlorophenoxy)acetic acid] was applied as foliar sprays at a rate of 2.24 kg AI ha(-1). Simulated rainfall was applied at an intensity of 29 mm h(-1) one day before and 1, 2, 4, and 8 days after the pesticide applications for 0.92, 1.75. 1.75, 0.92, and 0.92 h, respectively. Water run-off was measured using a tipping-bucket apparatus and sub-samples were analyzed for pesticide residues. Data collected from the study were also compared with the GLEAMS and PRZM-2 model simulations for surface water and 2,4-D run-off. Mass and concentration of 2,4-D in run-off decreased rapidly, with 74.5% of the total run-off of 2,4-D occurring in the first run-off event after treatment. When calibrated to the site-specific characteristics, the GLEAMS and the PRZM-2 models adequately simulated the average of surface water run-off over all plots, with normalized root mean square error (NRMSE) and coefficient of determination for linear regression (R(2)) being 22.8% and 0.917 for GLEAMS, and 23.7% and 0.879 for PRZM-2, respectively. However, both GLEAMS (NRMSE = 82.1%, R(2) = 0.776) and PRZM-2 (NRMSE = 125.8%, R(2) = 0.513) less accurately simulated 2,4-D concentrations in run-off. (C) 1999 Society of Chemical Industry.