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Articles by R. De Jong
Total Records ( 2 ) for R. De Jong
  H. Dadfar , S.E. Allaire , R. De Jong , E. van Bochove , J.-T Denault , G. Theriault and F. Dechmi
  Indicators of risk of water contamination by agricultural pollutants are developed in Canada to assess sustainability of agriculture. Crack flow (CF), a key pathway for sub-surface contaminant transport, is part of the transport-hydrology algorithm used in two of these risk indicators. The objective was to develop a methodology for predicting the likelihood of CF in Canadian agricultural soils at the landscape scale. The algorithm considers soil clay content, crack development followed by a runoff event based on water budget, tile drainage, and crops. More than 40% of Canadian farmlands had moderate to very high likelihood of CF, mainly in Manitoba, Ontario and Quebec, due to frequent runoffs on cracked clay soils potentially contributing to groundwater contamination. In Ontario and Quebec, farmlands with high CF likelihood correspond to regions under intensive tile drainage, which increases the risk of lateral translocation of contaminants to surface water bodies. Besides being a component of risk indicators of water contamination by phosphorus and coliforms, the CF algorithm and maps can be used to identify areas at risk of subsurface water contamination. Best management practices, adapted to reduce CF can then be targeted to these areas.
  H. Xu , R. De Jong , S. Gameda and B. Qian
  Spatially representative climate data are required input in various agricultural and environmental modelling studies. An agricultural ecodistrict climate database for Canada was developed from climate station data using a spatial interpolation procedure. This database includes daily maximum and minimum air temperatures, precipitation and incoming global solar radiation, which are necessary inputs for many agricultural modelling studies. The spatial interpolation procedure combines inverse distance squared weighting with the nearest neighbour approach. Cross-validation was performed to evaluate the accuracy of the interpolation procedure. In addition to some common error measurements, such as mean biased error and root mean square error, empirical probability distributions and accurate rates of precipitation occurrence were also examined. Results show that the magnitude of errors for this database was similar to those in other studies that used similar or different interpolation procedures. The average root mean square error (RMSE) was 1.7°C, 2.2°C and 3.8 mm for daily maximum and minimum temperature, and precipitation, respectively. The RMSE for solar radiation varied from 16 to 19% of the climate normal during April through September and from 21 to 28% of the climate normal during the remainder of the year.
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