Simulating soil nitrogen fate in irrigated crop production with manure applications.

Dairy manure is commonly applied to irrigated agricultural crops in the Magic Valley Region of southern Idaho, which has reported to impact the quality of surface and ground water. In this study, we used the Root Zone Water Quality Model (RZWQM2) to provide information about the long-term implications of manure applications. RZWQM2 was first calibrated and validated using 4 years of data from a long-term study with annual and biennial manure application rates of 18 Mg ha-1, 36 Mg ha-1, and 52 Mg ha-1, along with a control and conventional fertilizer treatment for crop yield, soil water and soil N. The 4-yr crop rotation was spring wheat (2013), potato (2014), spring barley (2015), and sugar beets (2016). RZWQM2 simulated soil water content, crop yield, total soil nitrogen, and soil nitrogen mineralization effectively as PBIAS and RRMSE for soil water content and crop yields were within the acceptable range (±25% for PBIAS and <1.0 for RRMSE). Nitrate in the soil profile was overestimated, however in the acceptable range for the validation treatments. The calibrated model was then run for 16 years by repeating the management practices of the 4-year scenarios (4 crop rotations) for all treatments and 24 years for the 52 T Annual treatment (6 crop rotations). The 16-year simulation results showed that nitrogen seepage from annual manure treatments (for example, 18 T Annual vs 18 T Biennial) was 2.0 to 2.3 times higher than the nitrogen seepage from the biennial manure treatments. Increasing manure applications from 18 T Annual to 52 T Annual increased N seepage an average of 3.2 times for the 16-year rotation. Nitrogen seepage increased dramatically in rotations 3 and 4 compared to rotations 1 and 2 in the sixteen-year simulation. The 24-year simulation results showed after manure had been applied annually for 16 years and then applications terminated, the amount of N seepage returned initial levels in 8 years. In conclusion, to maintain clean ground water, manure applications would be best applied biennially, and high applications should be discouraged.

Effects of residue removal and tillage on greenhouse gas emissions in continuous corn systems as simulated with RZWQM2.

Agricultural production is a major source of carbon dioxide (CO2) and nitrous oxide (N2O) globally. The effects of conservation practices on soil CO2 and N2O emissions remain a high degree of uncertainty. In this study, soil CO2 and N2O emissions under different residue and tillage practices in an irrigated, continuous corn system, were investigated using the Root Zone Water Quality Model (RZWQM2). Combinations of no/high stover removal (NR and HR, respectively) and no-till/conventional tillage (NT and CT, respectively) field experiments were tested over the four crop-years (Apr. 2011-Apr. 2015). The model was calibrated using the NRCT, and validated with other treatments. The simulation results showed that soil volumetric water content (VWC) in the NR treatments (i.e., NRCT and NRNT) was 1.3%-1.9% higher than that in the HR treatments (i.e., HRCT and HRNT) averaged across the four years. A higher amount of CO2 and N2O emissions were simulated in the NRCT across the four years (annual average: 7034 kg C/ha/yr for CO2 and 3.8 kg N/ha/yr for N2O), and lower emissions were in the HRNT (annual average: 6329 kg C/ha/yr and 3.7 kg N/ha/yr for N2O). A long-term simulation (2001-2015) suggested that the CO2 and N2O emissions were closely correlated with the stover removal degree (SRD), tillage, VWC, soil temperature (ST), years in management (Y), and fertilizer application. Stover and tillage practices had cumulative effects on CO2 emissions. The simulated annual CO2 emissions in 1st year from NRCT, NRNT, and HRCT were 7.8%, 0.0%, and 7.7% higher than that from HRNT, respectively; then the emissions in 15th year were 63.6%, 47.7%, and 29.1% higher, respectively. Meanwhile, there were no cumulative effects on N2O emissions. The results also demonstrated that the RZWQM2 is a promising tool for evaluating the long-term effects of CO2 and N2O emissions on different conservation practices.

Modeling and Mitigating Phosphorus Losses from a Tile-Drained and Manured Field Using RZWQM2-P.

Prediction of P losses from manured agricultural fields through surface runoff and tile drainage is necessary to mitigate widespread eutrophication in water bodies. However, present water quality models are weak in predicting P losses, particularly in tile-drained and manure-applied cropland. We developed a field-scale P management model, the Root Zone Water Quality Model version 2-Phosphorus (RZWQM2-P), whose accuracy in simulating P losses from manure applied agricultural field is yet to be tested. The objectives of this study were (i) to assess the accuracy of this new model in simulating dissolved reactive phosphorus (DRP) and particulate phosphorus (PP) losses in surface runoff and tile drainage from a manure amended field, and (ii) to identify best management practices to mitigate manure P losses including water table control, manure application timing, and spreading methods by the use of model simulation. The model was evaluated against data collected from a liquid cattle manure applied field with maize ( L.)-soybean [ (L.) Merr.] rotation in Ontario, Canada. The results revealed that the RZWQM2-P model satisfactorily simulated DRP and PP losses through both surface runoff and tile drainage (Nash-Sutcliffe efficiency > 0.50, percentage bias within ±25%, and index of agreement > 0.75). Compared with conventional management practices, manure injection reduced the P losses by 18%, whereas controlled drainage and winter manure application increased P losses by 13 and 23%, respectively. The RZWQM2-P is a promising tool for P management in manured and subsurface drained agricultural field. The injection of manure rather than controlled drainage is an effective management practice to mitigate P losses from a subsurface-drained field.

Climate Change Impacts on Yields and Soil Carbon in Row Crop Dryland Agriculture.

Dryland agroecosystems could be a sizable sink for atmospheric carbon (C) due to their spatial extent and level of degradation, providing climate change mitigation. We examined productivity and soil C dynamics under two climate change scenarios (moderate warming, representative concentration pathway [RCP] 4.5; and high warming, RCP 8.5), using long-term experimental data and the DayCent process-based model for three sites with varying climates and soil conditions in the US High Plains. Each site included a no-till cropping intensity gradient introduced in 1985, with treatments ranging from wheat-fallow ( L.) to continuous annual cropping and perennial grass. Simulations were extended to 2100 using data from 16 global circulation models to estimate uncertainty. Simulated yields declined for all crops (up to 50% for wheat), with small changes after 2050 under RCP 4.5 and continued losses to 2100 under RCP 8.5. Of the cropped systems, continuous cropping had the highest average productivity and soil C sequestration rates (78.1 kg C ha yr from 2015 to 2045 under RCP 4.5). Any increase in soil C for cropped rotations was realized by 2050, but grassland treatments increased soil C (up to 69%) through 2100, even under RCP 8.5. Our simulations indicate that reduced frequency of summer fallow can both increase annualized yields and store more soil C. As evapotranspiration is likely to increase, reducing fallow periods without live vegetation from dryland agricultural rotations may enhance the resilience of these systems to climate change while also increasing soil C storage and mitigating carbon dioxide emissions.

Corn stover harvest increases herbicide movement to subsurface drains - Root Zone Water Quality Model simulations.

BACKGROUND: Crop residue removal for bioenergy production can alter soil hydrologic properties and the movement of agrochemicals to subsurface drains. The Root Zone Water Quality Model (RZWQM), previously calibrated using measured flow and atrazine concentrations in drainage from a 0.4 ha chisel-tilled plot, was used to investigate effects of 50 and 100% corn (Zea mays L.) stover harvest and the accompanying reductions in soil crust hydraulic conductivity and total macroporosity on transport of atrazine, metolachlor and metolachlor oxanilic acid (OXA). RESULTS: The model accurately simulated field-measured metolachlor transport in drainage. A 3 year simulation indicated that 50% residue removal reduced subsurface drainage by 31% and increased atrazine and metolachlor transport in drainage 4-5-fold when surface crust conductivity and macroporosity were reduced by 25%. Based on its measured sorption coefficient, approximately twofold reductions in OXA losses were simulated with residue removal. CONCLUSION: The RZWQM indicated that, if corn stover harvest reduces crust conductivity and soil macroporosity, losses of atrazine and metolachlor in subsurface drainage will increase owing to reduced sorption related to more water moving through fewer macropores. Losses of the metolachlor degradation product OXA will decrease as a result of the more rapid movement of the parent compound into the soil. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Data worth and prediction uncertainty for pesticide transport and fate models in Nebraska and Maryland, United States.

BACKGROUND: Complex environmental models are frequently extrapolated to overcome data limitations in space and time, but quantifying data worth to such models is rarely attempted. The authors determined which field observations most informed the parameters of agricultural system models applied to field sites in Nebraska (NE) and Maryland (MD), and identified parameters and observations that most influenced prediction uncertainty. RESULTS: The standard error of regression of the calibrated models was about the same at both NE (0.59) and MD (0.58), and overall reductions in prediction uncertainties of metolachlor and metolachlor ethane sulfonic acid concentrations were 98.0 and 98.6% respectively. Observation data groups reduced the prediction uncertainty by 55-90% at NE and by 28-96% at MD. Soil hydraulic parameters were well informed by the observed data at both sites, but pesticide and macropore properties had comparatively larger contributions after model calibration. CONCLUSIONS: Although the observed data were sparse, they substantially reduced prediction uncertainty in unsampled regions of pesticide breakthrough curves. Nitrate evidently functioned as a surrogate for soil hydraulic data in well-drained loam soils conducive to conservative transport of nitrogen. Pesticide properties and macropore parameters could most benefit from improved characterization further to reduce model misfit and prediction uncertainty.

Simulating nitrate-nitrogen concentration from a subsurface drainage system in response to nitrogen application rates using RZWQM2.

Computer models have been widely used to evaluate the impact of agronomic management on nitrogen (N) dynamics in subsurface drained fields. However, they have not been evaluated as to their ability to capture the variability of nitrate-nitrogen (NO(3)-N) concentration in subsurface drainage at a wide range of N application rates due to possible errors in the simulation of other system components. The objective of this study was to evaluate the performance of Root Zone Water Quality Model2 (RZWQM2) in simulating the response of NO(3)-N concentration in subsurface drainage to N application rate. A 16-yr field study conducted in Iowa at nine N rates (0-252 kg N ha(-1)) from 1989 to 2004 was used to evaluate the model, based on a previous calibration with data from 2005 to 2009 at this site. The results showed that the RZWQM2 model performed "satisfactorily" in simulating the response of NO(3)-N concentration in subsurface drainage to N fertilizer rate with 0.76, 0.49, and -3% for the Nash-Sutcliffe efficiency, the ratio of the root mean square error to the standard deviation, and percent bias, respectively. The simulation also identified that the N application rate required to achieve the maximum contaminant level for the annual average NO(3)-N concentration was similar to field-observed data. This study supports the use of RZWQM2 to predict NO(3)-N concentration in subsurface drainage at various N application rates once it is calibrated for the local condition.

Verifiable metamodels for nitrate losses to drains and groundwater in the Corn Belt, USA.

Nitrate leaching in the unsaturated zone poses a risk to groundwater, whereas nitrate in tile drainage is conveyed directly to streams. We developed metamodels (MMs) consisting of artificial neural networks to simplify and upscale mechanistic fate and transport models for prediction of nitrate losses by drains and leaching in the Corn Belt, USA. The two final MMs predicted nitrate concentration and flux, respectively, in the shallow subsurface. Because each MM considered both tile drainage and leaching, they represent an integrated approach to vulnerability assessment. The MMs used readily available data comprising farm fertilizer nitrogen (N), weather data, and soil properties as inputs; therefore, they were well suited for regional extrapolation. The MMs effectively related the outputs of the underlying mechanistic model (Root Zone Water Quality Model) to the inputs (R(2) = 0.986 for the nitrate concentration MM). Predicted nitrate concentration was compared with measured nitrate in 38 samples of recently recharged groundwater, yielding a Pearson's r of 0.466 (p = 0.003). Predicted nitrate generally was higher than that measured in groundwater, possibly as a result of the time-lag for modern recharge to reach well screens, denitrification in groundwater, or interception of recharge by tile drains. In a qualitative comparison, predicted nitrate concentration also compared favorably with results from a previous regression model that predicted total N in streams.

Predicting unsaturated zone nitrogen mass balances in agricultural settings of the United States.

Unsaturated zone N fate and transport were evaluated at four sites to identify the predominant pathways of N cycling: an almond [Prunus dulcis (Mill.) D.A. Webb] orchard and cornfield (Zea mays L.) in the lower Merced River study basin, California; and corn-soybean [Glycine max (L.) Merr.] rotations in study basins at Maple Creek, Nebraska, and at Morgan Creek, Maryland. We used inverse modeling with a new version of the Root Zone Water Quality Model (RZWQM2) to estimate soil hydraulic and nitrogen transformation parameters throughout the unsaturated zone; previous versions were limited to 3-m depth and relied on manual calibration. The overall goal of the modeling was to derive unsaturated zone N mass balances for the four sites. RZWQM2 showed promise for deeper simulation profiles. Relative root mean square error (RRMSE) values for predicted and observed nitrate concentrations in lysimeters were 0.40 and 0.52 for California (6.5 m depth) and Nebraska (10 m), respectively, and index of agreement (d) values were 0.60 and 0.71 (d varies between 0 and 1, with higher values indicating better agreement). For the shallow simulation profile (1 m) in Maryland, RRMSE and d for nitrate were 0.22 and 0.86, respectively. Except for Nebraska, predictions of average nitrate concentration at the bottom of the simulation profile agreed reasonably well with measured concentrations in monitoring wells. The largest additions of N were predicted to come from inorganic fertilizer (153-195 kg N ha(-1) yr(-1) in California) and N fixation (99 and 131 kg N ha(-1) yr(-1) in Maryland and Nebraska, respectively). Predicted N losses occurred primarily through plant uptake (144-237 kg N ha(-1) yr(-1)) and deep seepage out of the profile (56-102 kg N ha(-1) yr(-1)). Large reservoirs of organic N (up to 17,500 kg N ha(-1) m(-1) at Nebraska) were predicted to reside in the unsaturated zone, which has implications for potential future transfer of nitrate to groundwater.

Soil nitrogen balance under wastewater management: field measurements and simulation results.

The use of treated wastewater for irrigation of crops could result in high nitrate-nitrogen (NO(3)-N) concentrations in the vadose zone and ground water. The goal of this 2-yr field-monitoring study in the deep silty clay loam soils south of Dodge City, Kansas, was to assess how and under what circumstances N from the secondary-treated, wastewater-irrigated corn reached the deep (20-45 m) water table of the underlying High Plains aquifer and what could be done to minimize this problem. We collected 15.2-m-deep soil cores for characterization of physical and chemical properties; installed neutron probe access tubes to measure soil-water content and suction lysimeters to sample soil water periodically; sampled monitoring, irrigation, and domestic wells in the area; and obtained climatic, crop, irrigation, and N application rate records for two wastewater-irrigated study sites. These data and additional information were used to run the Root Zone Water Quality Model to identify key parameters and processes that influence N losses in the study area. We demonstrated that NO(3)-N transport processes result in significant accumulations of N in the vadose zone and that NO(3)-N in the underlying ground water is increasing with time. Root Zone Water Quality Model simulations for two wastewater-irrigated study sites indicated that reducing levels of corn N fertilization by more than half to 170 kg ha(-1) substantially increases N-use efficiency and achieves near-maximum crop yield. Combining such measures with a crop rotation that includes alfalfa should further reduce the accumulation and downward movement of NO(3)-N in the soil profile.

Application of the Root Zone Water Quality Model (RZWQM) to pesticide fate and transport: an overview.

Pesticide transport models are tools used to develop improved pesticide management strategies, study pesticide processes under different conditions (management, soils, climates, etc) and illuminate aspects of a system in need of more field or laboratory study. This paper briefly overviews RZWQM history and distinguishing features, overviews key RZWQM components and reviews RZWQM validation studies. RZWQM is a physically based agricultural systems model that includes sub-models to simulate: infiltration, runoff, water distribution and chemical movement in the soil; macropore flow and chemical movement through macropores; evapotranspiration (ET); heat transport; plant growth; organic matter/nitrogen cycling; pesticide processes; chemical transfer to runoff; and the effect of agricultural management practices on these processes. Research to date shows that if key input parameters are calibrated, RZWQM can adequately simulate the processes involved with pesticide transport (ET, soil-water content, percolation and runoff, plant growth and pesticide fate). A review of the validation studies revealed that (1) accurate parameterization of restricting soil layers (low permeability horizons) may improve simulated soil-water content; (2) simulating pesticide sorption kinetics may improve simulated soil pesticide concentration with time (persistence) and depth and (3) calibrating the pesticide half-life is generally necessary for accurate pesticide persistence simulations. This overview/review provides insight into the processes involved with the RZWQM pesticide component and helps identify model weaknesses, model strengths and successful modeling strategies.

Documenting the pesticide processes module of the ARS RZWQM agroecosystem model.

We describe the theory and current development state of the pesticide process module of the USDA-Agricultural Research Service Root Zone Water Quality Model, or RZWQM. Several processes which are significant in determining the fate of a pesticide application are included together in this module for the first time, including application technique, root uptake, ionic dissociation, soil depth dependence of persistence, volatilization, wicking upward in soil and aging of residues. The pesticide module requires a large number of parameters to run (as does the RZWQM model as a whole) and it is becoming clear that RZWQM will find most interest and use as part of a 'scenario' in which all data requirements are supplied and the predictions of the system compared with a real (usually partial) data set. Such a scenario may then be modified to examine the response of the system to changes in inputs. It also has significant potential as a technology transfer or teaching tool, providing detailed understanding of a specific agronomic system and its potential impacts on the environment.

The pesticide module of the Root Zone Water Quality Model (RZWQM): testing and sensitivity analysis of selected algorithms for pesticide fate and surface runoff.

The Root Zone Water Quality Model (RZWQM) is a one-dimensional, numerical model for simulating water movement and chemical transport under a variety of management and weather scenarios at the field scale. The pesticide module of RZWQM includes detailed algorithms that describe the complex interactions between pesticides and the environment. We have simulated a range of situations with RZWQM, including foliar interception and washoff of a multiply applied insecticide (chlorpyrifos) to growing corn, and herbicides (alachlor, atrazine, flumetsulam) with pH-dependent soil sorption, to examine whether the model appears to generate reasonable results. The model was also tested using chlorpyrifos and flumetsulam for the sensitivity of its predictions of chemical fate and water and pesticide runoff to various input parameters. The model appears to generate reasonable representations of the fate and partitioning of surface- and foliar-applied chemicals, and the sorption of weakly acidic or basic pesticides, processes that are becoming increasingly important for describing adequately the environmental behavior of newer pesticides. However, the kinetic sorption algorithms for charged pesticides appear to be faulty. Of the 29 parameters and variables analyzed, chlorpyrifos half-life, the Freundlich adsorption exponent, the fraction of kinetic sorption sites, air temperature, soil bulk density, soil-water content at 33 kPa suction head and rainfall were most sensitive for predictions of chlorpyrifos residues in soil. The latter three inputs and the saturated hydraulic conductivity of the soil and surface crusts were most sensitive for predictions of surface water runoff and water-phase loss of chlorpyrifos. In addition, predictions of flumetsulam (a weak acid) runoff and dynamics in soil were sensitive to the Freundlich equilibrium adsorption constant, soil pH and its dissociation coefficient.

Modeling hydrology, metribuzin degradation and metribuzin transport in macroporous tilled and no-till silt loam soil using RZWQM.

Due to the complex nature of pesticide transport, process-based models can be difficult to use. For example, pesticide transport can be effected by macropore flow, and can be further complicated by sorption, desorption and degradation occurring at different rates in different soil compartments. We have used the Root Zone Water Quality Model (RZWQM) to investigate these phenomena with field data that included two management conditions (till and no-till) and metribuzin concentrations in percolate, runoff and soil. Metribuzin degradation and transport were simulated using three pesticide sorption models available in RZWQM: (a) instantaneous equilibrium-only (EO); (b) equilibrium-kinetic (EK, includes sites with slow desorption and no degradation); (c) equilibrium-bound (EB, includes irreversibly bound sites with relatively slow degradation). Site-specific RZWQM input included water retention curves from four soil depths, saturated hydraulic conductivity from four soil depths and the metribuzin partition coefficient. The calibrated parameters were macropore radius, surface crust saturated hydraulic conductivity, kinetic parameters, irreversible binding parameters and metribuzin half-life. The results indicate that (1) simulated metribuzin persistence was more accurate using the EK (root mean square error, RMSE = 0.03 kg ha(-1)) and EB (RMSE = 0.03 kg ha(-1)) sorption models compared to the EO (RMSE = 0.08 kg ha(-1)) model because of slowing metribuzin degradation rate with time and (2) simulating macropore flow resulted in prediction of metribuzin transport in percolate over the simulation period within a factor of two of that observed using all three pesticide sorption models. Moreover, little difference in simulated daily transport was observed between the three pesticide sorption models, except that the EB model substantially under-predicted metribuzin transport in runoff and percolate >30 days after application when transported concentrations were relatively low. This suggests that when macropore flow and hydrology are accurately simulated, metribuzin transport in the field may be adequately simulated using a relatively simple, equilibrium-only pesticide model.

Test of the Root Zone Water Quality Model (RZWQM) for predicting runoff of atrazine, alachlor and fenamiphos species from conventional-tillage corn mesoplots.

The Root Zone Water Quality Model (RZWQM) is a comprehensive, integrated physical, biological and chemical process model that simulates plant growth and movement of water, nutrients and pesticides in a representative area of an agricultural system. We tested the ability of RZWQM to predict surface runoff losses of atrazine, alachlor, fenamiphos and two fenamiphos oxidative degradates against results from a 2-year mesoplot rainfall simulation experiment. Model inputs included site-specific soil properties and weather, but default values were used for most other parameters, including pesticide properties. No attempts were made to calibrate the model except for soil crust/seal hydraulic conductivity and an adjustment of pesticide persistence in near-surface soil. Approximately 2.5 (+/- 0.9), 3.0 (+/- 0.8) and 0.3 (+/- 0.2)% of the applied alachlor, atrazine and fenamiphos were lost in surface water runoff, respectively. Runoff losses in the 'critical' events--those occurring 24 h after pesticide application--were respectively 91 (+/- 5), 86 (+/- 6) and 96 (+/- 3)% of total runoff losses for these pesticides. RZWQM adequately predicted runoff water volumes, giving a predicted/observed ratio of 1.2 (+/- 0.5) for all events. Predicted pesticide concentrations and loads from the 'critical' events were generally within a factor of 2, but atrazine losses from these events were underestimated, which was probably a formulation effect, and fenamiphos losses were overestimated due to rapid oxidation. The ratios of predicted to measured pesticide concentrations in all runoff events varied between 0.2 and 147, with an average of 7. Large over-predictions of pesticide runoff occurred in runoff events later in the season when both loads and concentrations were small. The normalized root mean square error for pesticide runoff concentration predictions varied between 42 and 122%, with an average of 84%. Pesticide runoff loads were predicted with a similar accuracy. These results indicate that the soil-water mixing model used in RZWQM is a robust predictor of pesticide entrainment and runoff.

Herbicide leaching as affected by macropore flow and within-storm rainfall intensity variation: a RZWQM simulation.

Within-event variability in rainfall intensity may affect pesticide leaching rates in soil, but most laboratory studies of pesticide leaching use a rainfall simulator operating at constant rainfall intensity, or cover the soil with ponded water. This is especially true in experiments where macropores are present--macroporous soils present experimental complexities enough without the added complexity of variable rainfall intensity. One way to get around this difficulty is to use a suitable pesticide transport model, calibrate it to describe accurately a fixed-intensity experiment, and then explore the affects of within-event rainfall intensity variation on pesticide leaching through macropores. We used the Root Zone Water Quality Model (RZWQM) to investigate the effect of variable rainfall intensity on alachlor and atrazine transport through macropores. Data were used from an experiment in which atrazine and alachlor were surface-applied to 30 x 30 x 30 cm undisturbed blocks of two macroporous silt loam soils from glacial till regions. One hour later the blocks were subjected to 30-mm simulated rain with constant intensity for 0.5 h. Percolate was collected and analyzed from 64 square cells at the base of the blocks. RZWQM was calibrated to describe accurately the atrazine and alachlor leaching data, and then a median Mid-west variable-intensity storm, in which the initial intensity was high, was simulated. The variable-intensity storm more than quadrupled alachlor losses and almost doubled atrazine losses in one soil over the constant-intensity storm of the same total depth. Also rainfall intensity may affect percolate-producing macroporosity and consequently pesticide transport through macropores. For example, under variable rainfall intensity RZWQM predicted the alachlor concentration to be 2.7 microg ml(-1) with an effective macroporosity of 2.2 E(-4) cm(3) cm(-3) and 1.4 microg ml(-1) with an effective macroporosity of 4.6 E(-4) cm(3) cm(-3). Percolate-producing macroporosity and herbicide leaching under different rainfall intensity patterns, however, are not well understood. Clearly, further investigation of rainfall intensity variation on pesticide leaching through macropores is needed.