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Published in J. Environ. Qual. 33:685-694 (2004).
© ASA, CSSA, SSSA
677 S. Segoe Rd., Madison, WI 53711 USA

TECHNICAL REPORT

Vadose Zone Processes and Chemical Transport

Concentration–Time Exposure Index for Modeling Soil Fumigation under Various Management Scenarios

D. Wang*,a, J. M. Hea and J. A. Knutesonb

a Department of Soil, Water, and Climate, University of Minnesota, St. Paul, MN 55108
b Dow AgroSciences, 9330 Zionsville Road, Indianapolis, IN 46268

* Corresponding author (wangd{at}umn.edu).

Received for publication August 5, 2002.

   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Best management decisions in soil fumigation require informed management selections of soil type, field geometry, application dosage, and depth to maximize fumigant distribution for efficacy and minimize off-site transport for environmental safety. An efficacy- or exposure-based concentration–time exposure index (CTEI) was used to serve as a continuous quantitative efficacy assessment for soil fumigation by subsurface drip irrigation using numerical model simulations. The CTEI was defined as the ratio between the soil volume where concentration–time (CT) exceeded a threshold value for a particular pest–fumigant combination and the total soil volume required for fumigation treatment. Applications of CTEI as a simple efficacy index were demonstrated by simulating combinations of three soil types (loam, sandy loam, sand); three field configurations consisting of 102- and 203-cm-wide bed systems and a flat surface system; three application depths (15, 30, 45 cm); and two application rates (82 and 327 kg ha–1) for 1,3-dichloropropene against citrus nematode (Tylenchulus semipenetrans) using a threshold air-phase CT value of 12 µg h cm–3 obtained from a separate field study. For soil fumigation by subsurface drip irrigation, the order of importance in optimizing CTEI was soil type, depth of application and depth of treatment, dosage, and field configuration. Model simulation using CTEI as a numeric efficacy index can be an effective alternative to assist in the planning of field trials for making final management decisions concerning soil fumigation or other pesticide applications.

Abbreviations: CT, concentration–time • CTEI, concentration–time exposure index • 1,3-D, 1,3-dichloropropene


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
PESTICIDE APPLICATION through irrigation systems (chemigation) has long been used for reduced operation cost and possible enhancement in pest control efficacy. Environmental concerns associated with soil fumigation have led to interest in applying fumigant chemicals through subsurface drip irrigation systems (Schneider et al., 1995; Wang et al., 2001). Because of reduced areas associated with drip irrigation (i.e., point or line sources) compared with the broadcast shank injection, the spatial and temporal distribution uniformity of a fumigant chemical in the soil and its potential efficiency for pest control becomes a concern. Variables that can significantly affect distribution uniformity and efficacy include the physical and chemical properties of fumigant chemicals, soil type, field geometry, application depth, and dosage. Ideally, field experiments should be conducted to test application procedures and to identify the optimum field geometry, application depth, and dosage for each pest–pesticide combination. However, it is time- and cost-prohibitive to measure every possible combination of pest–pesticide–soil management option. Mathematical model simulation, after validation, provides a powerful alternative for the selection and optimization of the management practices that will most likely produce the highest distribution uniformity for pest control, yet have the least potential for air and water contamination (van den Berg et al., 1999; Wang et al., 2000).

The product of pesticide concentration and time or CT has been used to numerically estimate pest control efficacy for various species of insect pests and plant diseases (Busvine, 1938; Muthu et al., 1975; Ben-Yephet et al., 1981; Su et al., 1989; Wang and Yates, 1999; Thomsen and Eilenberg, 2000). In soil fumigation by drip irrigation, the spatial and temporal concentration distribution of a fumigant chemical is highly variable in the subsurface porous soil (Schneider et al., 1995). Because of the dynamic nature of concentration variation controlled by gas diffusion and interactions with soil conditions and the application methods, data collection for the computation of the CT product is very difficult to carry out using direct field measurements and can only be made at discrete time intervals (Wang and Yates, 1999). Model simulation has the potential of readily generating CT outputs over nearly continuous temporal and spatial scales. Predictions from numerical simulations provide a capacity to screen various soil, fumigant, application method, and pest species combinations by adjusting initial and boundary conditions and input parameters. Because most soil-borne pest insects, diseases, and pathogens reside in different soil depths, a systematic means of determining pest control efficacy to a targeted soil depth is also needed. This study was conducted, by using computer model simulations, to (i) characterize fumigant distribution after application by drip irrigation and (ii) optimize pest control efficacy using a new efficacy- or exposure-based concentration–time exposure index (CTEI) with respect to soil type, field configuration, and application rate and depth. Using the new index, it is expected that the application rates may be optimized, leading to reduced cost and minimal fumigation-related environmental risks such as air and water contamination (Wang et al., 1997; Kotcon and Loria, 1987).


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Model Description
A two-dimensional finite-element code (Simunek and van Genuchten, 1994) was used for the simulation of fumigant dispersion in the soil profile. The model numerically solves the partial differential equations for two-dimensional nonlinear, nonequilibrium simultaneous transport of water and chemical (in both liquid and gaseous phases) in a variably saturated porous medium. A first-order decay reaction during transient water flow was applied to chemicals in both liquid and solid phases. The governing transport equation for a volatile fumigant chemical can be written as:

[1]
where CL, Cs, and Cg are fumigant concentrations in liquid (g cm–3), solid (g g–1), and gaseous (g cm–3) phases, respectively; {theta} is soil volumetric water content (cm3 cm–3); {rho} is soil bulk density (g cm–3); as is soil air content (cm3 cm–3); DL and Dg are respectively fumigant diffusion coefficients in liquid and gaseous phases (cm2 d–1); q is volumetric liquid flux density (cm d–1); µL and µs are first-order degradation rate constants in liquid and solid phases (d–1); t is time (d); and x and z are lateral and vertical distances (cm). Instantaneous equilibrium was used between CL and Cs by means of a linear adsorption coefficient (Kd), and between CL and Cg related by the modified Henry's law constant (Kh). The liquid flux density or q was calculated with the Richards equation for convective transport, which was the main mechanism for solution-phase flow. Degradation was considered in the solution (by hydrolysis) and adsorbed phases (by microbial metabolism), but not in the air, using a first-order decay having the same rate constant (µL = µs).

To simulate the application of a fumigant chemical through subsurface drip irrigation, a time-dependent flux input boundary condition was used, where:

[2]
and:

[3]
where Cf is the fumigant concentration in the irrigation stream (g cm–3), tf is fumigant application duration (d) and ti is the total duration of drip irrigation (d) where ti ≥ tf, and Q or Qi is the drip irrigation rate (cm3 d–1 cm–1 or cm2 d–1). Water input was continued for an additional time period (i.e., ti – tf) after chemical application to flush out the residual chemical in the irrigation system, a common practice in chemigation. The model has been used successfully in simulating fumigant transport in the soil (Wang et al., 2000), comparable with direct field measurements, which serves as a justification for its application in this study. The flow-related input parameters used in this study were: Qi = 15 cm2 d–1, tf = 0.25 d, and ti = 0.33 d.

Concentration–Time and Concentration–Time Exposure Index
The concentration–time (CT) product was determined as the integral of fumigant concentration with respect to time as:

[4]
where CT(to) is the concentration–time product (g d cm–3) up to time to (d) and Cx,z is the chemical concentration at a given location (x,z) in the soil air (g cm–3). Vapor-phase rather than the aqueous-phase concentration was used in calculating CT in this particular formulation. In fact, it would not matter what phases (vapor, aqueous, or solid) are used because instantaneous equilibrium was assumed between the three phases. However, it is important to maintain consistency or use the same phase CT values to determine the pest control threshold. The integration was evaluated numerically for every time increment in the simulation for every nodal point of the two-dimensional domain.

The concentration–time exposure index (CTEI) was defined as the ratio between the soil volume where CT exceeded a threshold value for a particular pest–fumigant combination and the total soil volume that was required for fumigation treatment. Obviously, CTEI is also time-dependent and can be described as:

[5]
where CTEI(to) is the concentration–time exposure index up to time to, Vc (cm3) is the volume with CT values exceeding a threshold value for a particular pest–fumigant combination, and Vt (cm3) is the total targeted soil volume required for fumigation treatment. Computation for CTEI was performed in the simulation with a numerical integration scheme to account for all nodal points and associate volumes where CT values exceeded the threshold. Clearly, CTEI has a dimensionless value within the range of 0 to 1, or 0 to 100%, with 0 as no treatment and 100% as complete pest control in the required soil volume. For illustration purpose, CTEI calculations in this study were based on 1,3-dichloropropene (1,3-D) efficacy against citrus nematode (Tylenchulus semipenetrans) using a threshold air-phase CT value of 12 µg h cm–3 obtained from a field experiment (Wang and Yates, 1999). The threshold CT was calculated for 100% mortality of citrus nematode using frequent concentration measurements of 1,3-D in soil air. Fate and transport related properties of 1,3-D (E isomer) used in the model input is listed in Table 1. For comparison purposes, the same adsorption and degradation rates averaged from literature values were used for different soil types. In real-case applications, these values should be determined for each soil–chemical combination. For example, the half-life of 1,3-D in sand can range from 5 to 23 d (van Dijk, 1980). Because a primary means of degradation for air-phase 1,3-D is by photochemical reactions with OH– radicals in the air (Tuazon et al., 1984), 1,3-D air-phase degradation below the soil surface should be negligible.


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  		Table 1. Fate and transport properties of 1,3-dichloropropene and hydraulic properties of three soil types used in the model simulation.

 
Currently, it is impossible to make direct validation comparisons for CTEI because there is no field or laboratory data available in the literature where efficacy is determined over depth or area of treatment. An example of comparison was made, with laboratory data from Xue et al. (2000), for simulated and measured CT values in relation to nematode survival. Briefly, 2.2 mg kg–1 1,3-D was added in 175-mL glass serum bottle that contained 50 g of soil and was maintained at 30°C. The volume occupied by soil, water, and air in the bottle was 19, 5, and 151 cm3, respectively. Subjected to instantaneous partition between the vapor, aqueous, and solid phases, and degradation in the aqueous and solid phases, simulated 1,3-D CT values tracked closely with the measured CT within 10 h after the treatment when about 80% mortality had occurred (Fig. 1) . Simulated CT values overpredicted CT between 24 and 48 h, and underpredicted CT at 96 h after treatment (Fig. 1).



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  		Fig. 1. Simulated and measured concentration–time (CT; mg h kg–1) in relation to nematode survival: a comparison with laboratory data from Xue et al. (2000).

 
Case Selection of Soil Type, Field Configuration, Application Rate, and Depth
Soil properties can significantly affect water and chemical transport, especially in chemigation through subsurface drip irrigation where water and pesticides are applied beneath the surface. Three hypothetical soil types (loam, sandy loam, and sand) were devised representing medium- to coarse-textured soils, typical for areas that often require soil fumigation. Hydraulic properties of the three soils (shown in Table 1) were based on a survey study with values averaged over 735 soil series for loam, 1183 for sandy loam, and 246 for sand, respectively (Carsel and Parrish, 1988).

Three field geometrical configurations were used to represent a 102-cm bed system with a single drip line located in the middle, a 203-cm bed system with two drip lines located at an equal distance from the bed center, and a flat surface with a single drip line in the middle at 203-cm spacing. Dimensions for the 102-cm bed system were 89-cm width for bed base, 45-cm width for bed top, and 19 cm for bed height. Dimensions for the 203-cm bed system were 190-cm width for bed base, 146-cm width for bed top, and 19 cm for bed height. For each configuration, the drip line was placed 15, 30, or 45 cm deep, respectively, simulating three application depths. The modeled application rates for 1,3-D were the typical 327 kg ha–1 and a reduced rate at 82 kg ha–1. A total of 54 scenarios were simulated. Variable grid sizes were used for numerical stability, and the average maximum number of nodal points in the two-dimensional domains was about 14000.

Uncertainty Analysis
Because hypothetical soils were used in the simulations, an uncertainty analysis was performed to assess the reliability of the estimated CTEI for each soil type. A joint multivariate distribution model was used to generate 1000 sets of autocorrelated soil water retention parameters for loam, sandy loam, and sand, respectively. To facilitate the computation of CTEI for each realization, a separate code (named as ParGen) was developed, similar to that of Carsel and Parrish (1988), and integrated in the two-dimensional finite-element code of Simunek and van Genuchten (1994). Briefly, Monte Carlo simulations were used in ParGen to generate each set of parameters with fixed means and covariances for each soil type. These mean and covariance values and the type of probability distribution function for a particular parameter were determined, by Carsel and Parrish (1988), from the same measured datasets selected for the hydraulic parameters (Table 1). The program, ParGen, also performed inversion of the Johnson transformations (Johnson and Kotz, 1970), for each parameter, from normal to log normal, log ratio, or hyperbolic arcsine distributions before input in the two-dimensional model. The uncertainty analysis was performed for the 102-cm-wide beds, 30-cm application depth, and 327 kg ha–1 application rate case and repeated 1000 times for loam, sandy loam, and sand, respectively. Mean, standard deviation, and coefficient of variance of CTEI were computed for each timed output and compared by soil type.

To facilitate the relatively large number of runs, simulations were conducted on a 370-processor IBM SP supercomputer at the University of Minnesota Supercomputing Institute. The simulation could have been done on IBM compatible PCs, but would require a much longer time to complete all the scenarios.


   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
CONCLUSIONS
 REFERENCES
 
Spatial and Temporal Dynamics of Concentration and Concentration–Time
Concentrations of 1,3-D in soil air exhibited a concentric distribution pattern around the drip line within about 7 d after application at a 30-cm depth under a 102-cm field bed (Fig. 2a, 2b, and 2c) . The distribution pattern was expected and was similar to direct field observations based on soil-air sampling of an earlier study (Wang and Yates, 1999). The center mass, possessing a concentration value of greater than 50 µg cm–3 (Fig. 2a), had moved to about a 40-cm depth by Day 3, due to the gravitational effect on water movement. Concentrations above 1 µg cm–3 reached a soil depth of 75 to 90 cm. At 5 and 7 d after application, more dispersion occurred and the 1 µg cm–3 concentration range reached approximately the 100- and 110-cm depths, respectively (Fig. 2b and 2c). Because of volatilization losses, 1,3-D concentration in the top 5 to 10 cm of soil was less than 1 µg cm–3 at 10 d after application; however, the residual concentration still exceeded 2 µg cm–3 for a majority of the soil profile (Fig. 2d).



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  		Fig. 2. Spatial distribution of air-phase concentration (µg cm–3) of 1,3-dichloropropene under a 102-cm-wide field bed at (a) 3, (b) 5, (c) 7, and (d) 10 d after application by drip irrigation at a 30-cm depth and 327 kg ha–1 in a sandy loam soil.

 
Under the typical application rate of 327 kg ha–1, the CT product values exceeded the threshold (12 µg h cm–3) against citrus nematode up to 75-, 105-, and 130-cm depths at 3, 5, and 7 d after application by drip irrigation (Fig. 3a, 3b, and 3c) . The CT values exceeded 100 µg h cm–3 for the top 100 cm of soil at 10 d after application (Fig. 3d). These results indicate that applying 1,3-D by drip irrigation in a sandy loam soil at a 30-cm depth and 327 kg ha–1 rate would easily achieve 100% efficacy against citrus nematode for the top 50 cm in 3 d and for the top 100 cm of soil in 5 d. The large CT values also indicate that an application dosage lower than 327 kg ha–1 would likely provide sufficient control for 1,3-D against citrus nematode. The noncircular shape of CT contour for values that equal or exceed 10000 µg h cm–3 during the first 7 d after application (Fig. 3a, 3b, and 3c) was attributed to gravity effect on soil solution and the shape of drip tapes (horizontally placed) as the source of 1,3-D input.



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  		Fig. 3. Concentration–time (CT, µg h cm–3) of 1,3-dichloropropene under a 102-cm-wide field bed at (a) 3, (b) 5, (c) 7, and (d) 10 d after application by drip irrigation at a 30-cm depth and 327 kg ha–1 in a sandy loam soil.

 
For the same dosage (327 kg ha–1) and depth (30 cm) of application but applied through two drip lines under a 203-cm bed, a slightly reduced distribution uniformity was observed (Fig. 4) compared with the 102-cm bed system (Fig. 2). The potential lack of treatment was in areas farthest away from the drip lines. Therefore, increasing the spacing between the two drip lines would likely improve the distribution uniformity. A similar distribution pattern was found for the CT values (Fig. 5) where the threshold CT value (12 µg h cm–3) reached over 90 and 115 cm at 5 and 7 d after application, respectively (Fig. 5b and 5c).



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  		Fig. 4. Spatial distribution of air-phase concentration (µg cm–3) of 1,3-dichloropropene under a 203-cm-wide field bed at (a) 3, (b) 5, (c) 7, and (d) 10 d after application by drip irrigation at a 30-cm depth and 327 kg ha–1 in a sandy loam soil.

 


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  		Fig. 5. Concentration–time (CT, µg h cm–3) of 1,3-dichloropropene under a 203-cm-wide field bed at (a) 3, (b) 5, (c) 7, and (d) 10 d after application by drip irrigation at a 30-cm depth and 327 kg ha–1 in a sandy loam soil.

 
Effects of Soil Type on Concentration–Time Exposure Index and Simulation Reliability
Because of the temporal dependency in CT, simulated 1,3-D CTEI against citrus nematode increased over time after fumigation, and asymptotically approached 100% for the top-100-cm soil profile (Fig. 6a) . The time required to reach 100% CTEI was related to soil type: the shortest (3.5 d) for sand and the longest (>10 d) for loam (Fig. 6a). Differences in soil type or texture can significantly affect water flow that controls the solution-phase convective transport of dissolved soil fumigants. Faster movement is possible in coarse-textured soils such as sand. The average hydraulic conductivity under saturated conditions for sand was 6.7 and 28.5 times of that for sandy loam and loam, respectively (Table 1). Coarse-textured soils also have a higher drainage rate characterized by the relatively larger hydraulic parameters ({alpha} and n) than sandy loam or loam (Table 1); therefore, the air content in sand should be greater than sandy loam and loam soon after irrigation. This would increase the effective gas diffusion rate through air spaces in the soil. The effective gas-phase diffusion rate is proportional to the air content: higher air content, faster diffusion. Therefore, both solution- and gaseous-phase fumigant transports were enhanced in coarse-textured soils, which required the least amount of time to achieve high CTEI. These minimum time requirements for 100% CTEI should be considered when determining the treatment duration for soil fumigation in different soil types.



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  		Fig. 6. Soil type effects on concentration–time exposure index (CTEI) (a) mean, (b) standard deviation or SD, and (c) coefficient of variance or CV of the top-100-cm profile calculated from 1000 realizations using a Monte Carlo procedure for 1,3-dichloropropene efficacy against citrus nematode (Tylenchulus semipenetrans) using a threshold air-phase CT value of 12 µg h cm–3 (Wang and Yates, 1999). Fields are 102-cm-wide beds. Application depth = 30 cm; application dosage = 327 kg ha–1.

 
The uncertainty analysis, based on the joint multivariate distribution model with 1000 integrated Monte Carlo and deterministic simulations for each soil type, showed relatively lower standard deviation (SD) (Fig. 6b) and coefficient of variance (CV) (Fig. 6c) values for the coarse-textured sand and sandy loam soils than for the loam soils. Because the simulated CTEI approached 100% at about 3.5 and 5.0 d for sand and sandy loam, respectively, both SD and CV decreased to zero. The overall relatively low SD (<10%) and CV (<25%) values for sand and sandy loam indicated that simulations with hypothetical coarse-textured soils were very reliable. For loam, the uncertainty was relatively large (CV up to 45%) near the beginning of the simulation, but gradually decreased to less than 17% after 5 d (Fig. 6c). The convergence of CV values (toward zero) for all three soil types indicated that the overall performance of the simulations performed in this study was numerically reliable.

Effects of Field Configuration on Concentration–Time Exposure Index
For the same rate and depth of fumigant application by drip irrigation, 1,3-D CTEI against citrus nematode increased on a similar time course for the 102- and 203-cm bed systems, with both exceeding 90% about 3.5 d after application (Fig. 7) . A slower increase, compared with the two-bedded systems, was found for the flat-field configuration that had one drip line spaced at a 203-cm distance. The exact time required to reach 100% CTEI in the top-100-cm soil profile was 4.5, 7.5, and 9.7 d for the 102-cm bed, 203-cm bed, and flat-field configurations, respectively. The increase in time requirement between the three configurations was attributed to two factors: (i) increased distance from the source to the farthest point of the soil surface and (ii) increased total treatment area in the two-dimensional domain. The farthest distance from the source to the soil surface was 52, 72, and 106 cm for the 102-cm and 203-cm beds and flat field, respectively. The total treatment area for the top 100 cm of soil (measured from bed center downward) increased from 83% of the two-dimensional domain for the 102-cm bed, to 92% for the 203-cm bed, and 100% for the flat-field configuration. Clearly, variations in field configuration will affect fumigant distribution uniformity and ultimately CTEI. A modeling prediction can guide the selection of a field geometry that will result in the most uniform distribution.



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  		Fig. 7. Field configuration effects on concentration–time exposure index (CTEI) of the top-100-cm profile of a sandy loam soil calculated for 1,3-dichloropropene efficacy against citrus nematode (Tylenchulus semipenetrans) using a threshold air-phase CT value of 12 µg h cm–3 obtained from a field study (Wang and Yates, 1999). Application depth = 30 cm; application dosage = 327 kg ha–1.

 
Effects of Application Rate on Concentration–Time Exposure Index
Theoretically, CTEI is related to dosage, but may be nonlinear. A 75% dose reduction from the conventional 327 kg ha–1 rate only slightly reduced 1,3-D CTEI against citrus nematode (Fig. 8) . The time required to reach 100% CTEI for the 82 kg ha–1 application rate increased to 6.5 d, compared with 4.5 d for the 327 kg ha–1 rate. The relative insensibility in CTEI with respect to dosage was attributed to the effectiveness of 1,3-D against citrus nematode or a relatively small threshold CT value (12 µg h cm–3; Wang and Yates, 1999). If the soil surface is covered by a plastic containment film, further reductions in dosage are possible without seriously affecting efficacy or CTEI. These results indicate the potential of reducing the labeled dosage in soil fumigation by subsurface drip irrigation and the benefit of using CTEI in a modeling framework to optimize application rate without sacrificing either efficacy or environmental safety. Results shown in Fig. 8 were based on CTEI of the top-100-cm profile of a sandy loam soil calculated for 1,3-D efficacy against citrus nematode using 102-cm-wide field beds and 30-cm application depth. Potential dosage reduction for other fumigant, pest species, and field soil and application scenarios should be evaluated on a case-by-case basis. Again, a modeling approach with CTEI as the benchmark for efficacy assessment would be one of the most effective means for making management decisions.



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  		Fig. 8. Dosage effects on concentration–time exposure index (CTEI) of the top-100-cm profile of a sandy loam soil calculated for 1,3-dichloropropene efficacy against citrus nematode (Tylenchulus semipenetrans) using a threshold air-phase CT value of 12 µg h cm–3 obtained from a field study (Wang and Yates, 1999). Fields are 102-cm-wide beds. Application depth = 30 cm.

 
Effects of Application Depth on Concentration–Time Exposure Index
For the top 100 cm of soil measured at bed center of the 102-cm bed system, 1,3-D CTEI against citrus nematode increased with increasing the application depth (Fig. 9) . The time required to reach 100% CTEI decreased from 6.0 to 4.5 to 3.5 d when increasing the application depth from 15 to 30 to 45 cm, respectively (Fig. 9). This was expected because fumigant dissipation was dominated by gas diffusion, a process driven by concentration gradient where gravity would have minimal effect on the direction and the rate of dispersion. Furthermore, application at a 45-cm depth was closer to the center depth of the targeted 100-cm profile than the 15- and 30-cm applications. Had the integration for CTEI been aimed for the surface 30-cm profile, the 15-cm-depth application would have been the most effective. Selection of an optimum application depth by drip irrigation should also consider the depth of infestation of soil-borne pest species.



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  		Fig. 9. Effects of application depth on concentration–time exposure index (CTEI) of the top-100-cm profile of a sandy loam soil calculated for 1,3-dichloropropene efficacy against citrus nematode (Tylenchulus semipenetrans) using a threshold air-phase CT value of 12 µg h cm–3 obtained from a field study (Wang and Yates, 1999). Fields are 102-cm-wide beds. Application dosage = 327 kg ha–1.

 
Interactive Effects of Soil Type, Field Configuration, and Depth on Concentration–Time Exposure Index
Application of CTEI also provides a continuous quantitative measure to simultaneously consider variables related to soil type, field configuration, depth of fumigation, and possibly equipment limitations. The interactive effects of these variables on CTEI are summarized in Table 2 with the top 50 cm as the targeted fumigation area, and in Table 3 for treating the top 100 cm of soil. Overall, soil type was the dominant factor where higher values of CTEI were obtained in sand than in sandy loam or loam regardless of differences in field configuration, application depth, or depth of treatment. Therefore, for soil fumigation by drip irrigation in very coarse-textured soils such as sand, selection of field configuration, and depth of application is less critical than in medium-textured soils such as loam.


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  		Table 2. Concentration–time exposure index (CTEI) for the top-50-cm soil profile.{dagger}

 

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  		Table 3. Concentration–time exposure index (CTEI) for the top-100-cm soil profile.{dagger}

 
There was a strong interactive effect between application depth and the depth of treatment on CTEI. The highest CTEI values were obtained for the 30-cm application targeted for the top 50 cm of soil and 100% CTEI was achieved in 3 d for the 30-cm application in sand (Table 2). The highest CTEI values were found for the 45-cm application targeted for the top 100 cm of soil (Table 3). Selection of an optimum application depth by drip irrigation should consider the depth of occurrences of soil-borne pest species.

For the same soil type, a narrower bed width or drip line spacing such as the 102-cm bed system created the highest CTEI, compared with the 203-cm spaced bed or flat-field configurations. This effect was also more important in loam than in sand.


   CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
REFERENCES
 
A new uniformity- and efficacy-based concentration–time exposure index (CTEI) was developed to provide a continuous quantitative assessment of soil fumigation by subsurface drip irrigation using mathematical model simulations. Pest control efficacy, measured by the numerical values of CTEI, was compared for various management options with respect to soil type, field configuration, and application rate and depth. For soil fumigation by subsurface drip irrigation, the order of importance of management options on CTEI was soil type, depth of application and depth of treatment, dosage, and field configuration. Model simulation using CTEI as a numeric variable can be used as an effective alternative to help make management decisions for soil fumigation by drip irrigation. The model prediction can be improved by inclusion of temperature effects on efficacy (Bell, 1992; Xue et al., 2000) in calculating CTEI. The modeling approach using CTEI is limited by the availability of threshold CT values for pest–pesticide combinations. Additional model testing, especially for CTEI correlation with direct field and laboratory measurements, will provide further validation for the modeling approach of predicting the uniformity, efficacy, and environmental fate and transport of soil fumigant chemicals.


   ACKNOWLEDGMENTS
 
We thank Dr. Ian van Wesenbeeck for reviewing the manuscript before submission and providing valuable comments for improvement. We are also very grateful to Ryan Abo for help on carrying out the uncertainty analysis on effects of different soil types on CTEI. Partial support by Dow AgroSciences and the University of Minnesota Supercomputing Institute is also greatly appreciated.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 


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JEQ 2004 33: 413-418. [Full Text]  




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