Uncertainty in Pesticide Leaching Risk Due to Soil Variability

2015 ◽  
pp. 99-129
Author(s):  
David L. Nofziger ◽  
Jin-Song Chen ◽  
Arthur G. Hornsby
Keyword(s):  
Soil Variability ◽  
Leaching Risk ◽  
Pesticide Leaching

Assessment of the pesticide leaching risk at the Pan-European level. The EuroPEARL approach

2004 ◽  
Vol 289 (1-4) ◽  
pp. 222-238 ◽  
Author(s):  
A Tiktak ◽  
D.S de Nie ◽  
J.D Piñeros Garcet ◽  
A Jones ◽  
M Vanclooster
Keyword(s):  
European Level ◽  
Leaching Risk ◽  
Pesticide Leaching

Effect of soil variability, within and between soil taxonomic units, on simulated nitrate leaching under arable farming, New Zealand

Soil Research ◽  
2002 ◽  
Vol 40 (7) ◽  
pp. 1187 ◽  
Author(s):  
L. R. Lilburne ◽  
T. H. Webb
Keyword(s):  
Organic Matter ◽  
Nitrate Leaching ◽  
Soil Depth ◽  
Water Storage ◽  
Organic Matter Content ◽  
Matter Content ◽  
Distribution Functions ◽  
Soil Variability ◽  
Soil Series ◽  
Leaching Risk

A Monte Carlo approach was used to predict the effect of soil variability on nitrate leaching from 8 soil series, encompassing a wide range of drainage, texture, and age of soil development characteristics. A database of soil physical properties consisting of a minimum of 9 profiles per soil series was used to derive correlated probability distribution functions of key soil properties. The distribution functions were then used for random sampling to derive input soil-data for the GLEAMS (Groundwater Loading Effects of Agricultural Management Systems) simulation model. Variability in soil properties found within single soil taxonomic units (depth/stoniness phases of soil series) resulted in an appreciable range of predicted leaching of nitrate. Leaching from soils with greatest variability (generally shallow and stony phases) had an inter-quartile range of predicted leaching of up to 19 kg N/ha in 1992. Sensitivity analysis indicated that organic matter content, depth to the base of the upper 2 horizons, and available water storage were important drivers of variability within soil taxonomic units. Despite wide variation within soil taxonomic units, there were still clear differences between them. Effective soil depth accounted for most of this variance, which was attributed to differences in total profile available water storage. Soil drainage had some effect on risk of leaching. This effect would probably have been greater if water table effects had been accounted for. Soil series distinctions related to soil age had no significant effect of leaching risk. These results indicate that nitrogen leaching risk assessment using GLEAMS is dependent upon soil maps with accurate identification of soil depth/stoniness phases and organic matter content.

scholarly journals An indicator of pesticide leaching risk to groundwater

2012 ◽  
Vol 23 ◽  
pp. 95-108 ◽  
Author(s):  
Anna M.L. Lindahl ◽  
Christian Bockstaller
Keyword(s):  
Leaching Risk ◽  
Pesticide Leaching

Automation and evaluation of three pesticide fate models for a national analysis of leaching risk in Canada

2007 ◽  
Vol 87 (Special Issue) ◽  
pp. 203-212 ◽  
Author(s):  
D. A. R. McQueen ◽  
A. Farenhorst ◽  
S. Allaire ◽  
A J Cessna
Keyword(s):  
Water Contamination ◽  
Application Rate ◽  
Risk Indicators ◽  
Soil Series ◽  
Leaching Potential ◽  
Agricultural Pesticides ◽  
Fate Model ◽  
National Analysis ◽  
Leaching Risk ◽  
Pesticide Leaching

Under the National Agri-Environmental Health Analysis and Reporting Program (NAHARP), pesticide fate models are being used to develop an indicator of risk of water contamination by pesticides (IROWC-Pest) in Canada. The large number of model runs needed for a national analysis of the risk of pesticide leaching to ground water required the development of a computer program, AutoPFM (Automate Pesticide Fate Model), to automate the running of pesticide fate models. Using Manitoba as a test province, and the selected pesticide fate models PRZM, LEACHP, and MACRO, AutoPFM permitted the estimation of the leaching potential of the fourteen most used agricultural pesticides in Manitoba. Assuming an application rate of 300 g ha-1 for each pesticide, only six pesticides demonstrated leaching across most soil series. For these six pesticides, there was significant correlation in how PRZM and LEACHP ranked the leaching potential of 337 Manitoba soil series. Because of its long running times, the estimation of leaching potential with MACRO was limited to two pesticides (2,4-D and MCPA). For these pesticides, MACRO showed significant correlation with the PRZM and LEACHP in ranking the soil series. Based on the results described in this paper, PRZM was chosen as the pesticide leaching model for use in IROWC-Pest. Key words: Risk indicators, pesticide, PRZM, LEACHM, LEACHD, MACRO, model automation, leaching, soil

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