Pesticide Runoff Simulations: Long-Term Annual Means vs. Event Extremes?

Leonard, R. A.; Truman, C. C.; Knisel, W. G.; Davis, Frank M.

doi:10.1017/s0890037x00036113

Cited by 11 publications

(7 citation statements)

References 5 publications

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“…The largest runoff event was on April 14 and 15, when 210 g ha Ϫ1 of atrazine was lost from recently applied EPP treatments, and 60 g ha Ϫ1 was lost from the FALL application (Figure 2c). Although these were not statistically different, as a result of variability among plots, these trends demonstrate how atrazine runoff loss may decrease with increasing time interval from application to the runoff event, as previously reported by Baker and Mickelson (1994), Hall (1974), and Leonard et al (1992).…”

Section: -1999supporting

confidence: 77%

Application timing impact on runoff losses of atrazine

Rector

Regehr²,

Barnes

et al. 2003

Weed Science

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Field experiments were conducted from 1996 to 2000 near Manhattan, KS, to determine the effects of application timing on atrazine loss in surface water runoff. In addition, Groundwater Loading Effects of Agricultural Management Systems (GLEAMS) was run to compare simulated loss with actual loss in the field. Atrazine treatments were fall plus preemergence (FALL + PRE), early preplant plus PRE (EPP + PRE), PRE at a low rate (PRE-LOW), and PRE at a full (recommended) rate (PRE-FULL). Ridge-till furrows served as mini watersheds for the collection of surface water runoff. Water runoff volumes and herbicide concentrations were determined for each runoff event. Across four sampling years, mean atrazine runoff loss was 1.7, 4.3, and 1.7% of applied for FALL + PRE, EPP + PRE, and the mean of the PRE treatments, respectively. Thus, actual average losses from FALL + PRE and EPP + PRE treatments were somewhat higher than that predicted by GLEAMS. For PRE treatments, actual average losses were significantly lower than that predicted by GLEAMS, with measured losses falling below the bottom of the graph in 3 of 4 yr. These findings suggest that in certain parts of the Great Plains, FALL + PRE split applications of atrazine offer acceptably low atrazine runoff loss potential; EPP + PRE is more vulnerable to loss than FALL + PRE; and the GLEAMS model may overestimate atrazine runoff potential for PRE applications.

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Section: -1999supporting

confidence: 77%

Application timing impact on runoff losses of atrazine

Rector

Regehr²,

Barnes

et al. 2003

Weed Science

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“…A similar sensitivity analysis on a sand‐textured soil showed the same relative magnitude of effect, but the absolute amount leached was considerably more significant (pesticide leached increased from 12–35%). Leonard et al [16] presented similar conclusions regarding annual runoff losses for pesticides as predicted by GLEAMS (rainfall, especially in a short postapplication interval, has an overriding importance on runoff predictions). These authors simulated moderately to strongly sorbed pesticides ( K oc of 100–1,000) with short soil half‐lives (15 d) and also stressed that as half‐life increased, sensitivities to inputs such as rainfall might decrease and/or variables related to sediment transport might increase in importance.…”

Section: Introductionmentioning

confidence: 77%

Sensitivity analysis for validating expert opinion as to ideal data set criteria for transport modeling

Wolt¹,

Singh

Cryer³

et al. 2002

Enviro Toxic and Chemistry

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Environmental fate modeling results are often used in risk assessment without adequately considering uncertainty in exposure predictions. Sensitivity analysis is fundamental to model validation and error prediction since sensitive model input parameters account for the largest variance in model prediction. Once identified, sensitive model input parameters can be used to propagate parametric uncertainty in numerical predictions. Output sensitivity to variation in input code sequences was investigated for the pesticide root zone model (PRZM 3) using Plackett-Burman analysis for six runoff and leaching data sets. The analysis utilized an incomplete block factorial design with even parameter weighting and uniform proportional input perturbation. Timing and duration of key period rainfall were assumed a priori to be dominant sensitive inputs. Thus, meteorological data were fixed, allowing identification of additional input components contributing to model sensitivity. Results validated expert modeler assumptions concerning parameters most critical for model validation. For leaching data sets, the application rate, soil bulk density (an indicator of available water-holding capacity), chemical partition coefficient, and pesticide degradation rates were commonly the most sensitive inputs. For runoff data sets, the in-crop runoff curve number was the most significant input governing pesticide loss in runoff and erosion flux. The chemical partition coefficient, soil and foliar decay rates, and soil bulk density were also common sensitive components for runoff predictions. These commonly observed sensitive components for runoff and leaching prediction need to be carefully considered in the design and conduct of relevant field studies, modeling assessment of such studies, and future improvements in algorithms for environmental transport modeling.

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“…Bars with the same letters within a sampling date and individual graph are not different according to Fisher's Protected LSD test at P ϭ 0.05. razine, S-metolachlor, and isoxaflutole/DKN (Figure 1). It is well documented that, given a series of similar precipitation events after atrazine application, runoff losses are greatest in the first runoff event and decline with each subsequent event (Gaynor et al 1992(Gaynor et al , 1995Leonard et al 1992;Pantone et al 1992;Tasli et al 1996). Several of these reports involved simulated rainfall, which would guarantee similar precipitation events.…”

Section: Resultsmentioning

confidence: 99%

Atrazine,S-metolachlor, and isoxaflutole loss in runoff as affected by rainfall and management

et al. 2003

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Research was conducted to determine the effects of management practices and precipitation on herbicide loss in surface water runoff. A field runoff experiment was conducted in 1999 and 2000 in Manhattan, KS. Some plots received only natural precipitation, whereas others received natural precipitation plus additional precipitation from a rainfall simulator. Atrazine was applied at 0.9 and 1.8 kg ha−1, S-metolachlor at 0.7 and 1.4 kg ha−1, and isoxaflutole at 0.05 and 0.11 kg ha−1 to field corn grown under conventional tillage and no-till. Runoff volumes and herbicide concentrations were determined for each runoff event. Across all precipitation, tillage, and placement variables, atrazine, S-metolachlor, and isoxaflutole and diketonitrile (DKN) (soil metabolite of isoxaflutole), hereafter referred to as isoxaflutole/DKN, losses were similar at 5.0, 4.1, and 4.1% of applied, respectively. Additional precipitation increased runoff 2.5-, 2.2-, and 3.4-fold for atrazine, S-metolachlor, and isoxaflutole/DKN, respectively. Preplant soil incorporation reduced atrazine, S-metolachlor, and isoxaflutole/DKN losses in runoff by 67, 69, and 57%, respectively, compared with soil surface applications. Lower preplant rainfall in 2000 resulted in sharply reduced runoff losses despite postplant precipitation similar to that in 1999. These findings suggest that the best management practices for atrazine can be used to manage S-metolachlor and isoxaflutole/DKN loss in surface water runoff.

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Pesticide Runoff Simulations: Long-Term Annual Means vs. Event Extremes?

Cited by 11 publications

References 5 publications

Application timing impact on runoff losses of atrazine

Application timing impact on runoff losses of atrazine

Sensitivity analysis for validating expert opinion as to ideal data set criteria for transport modeling

Atrazine,S-metolachlor, and isoxaflutole loss in runoff as affected by rainfall and management

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