Search

Preferential flow is a widespread phenomenon that is known to strongly affect solute transport in soil, but our understanding and knowledge is still poor of the site factors and soil properties that promote it. To investigate these relationships, we assembled a database from the peer-reviewed literature containing information on 793 breakthrough curve experiments under steady-state flow conditions. Most of the collected experiments (642 of the 793 datasets) had been conducted on undisturbed soil columns, although some experiments on repacked soil, clean sands, and glass beads were also included. In addition to the apparent dispersivity, we focused attention on three potential indicators of preferential solute transport, namely the 5 %-arrival time, the holdback factor, and the ratio of piston-flow and average transport velocities. Our results suggest that in contrast to the 5 %-arrival time and the holdback factor, the piston-flow to transport velocity ratio is not related to preferential macropore transport but rather to the exclusion or retardation of the applied tracer. Confirming that the apparent longitudinal dispersivity is positively correlated with the travel distance of the tracer, our results also illustrate that this correlation is refined if the normalized 5 %-tracer arrival time is also taken into account. In particular, we found that the degree of preferential solute transport increases with apparent dispersivity and decreases with travel distance. A similar but weaker relationship was observed between apparent dispersivity, 5 %-tracer arrival time, and lateral observation scale, such that the strength of preferential transport increases with lateral observation scale. However, we also found that the travel distance and the lateral observation scale in the investigated dataset are correlated which makes it difficult to distinguish their influence on these transport characteristics. We observed that anionic tracers exhibited larger apparent dispersivities than electrically neutral tracers under comparable experimental conditions. We also found that the strength of preferential transport increased at larger flow rates and water saturations, which suggests that macropore flow was a more important flow mechanism than heterogeneous flow in the soil matrix. Nevertheless, our data shows that heterogeneous flow in the soil matrix also occasionally leads to strong preferential transport. Furthermore, we show that preferential solute transport under steady-state flow depends on soil texture in a threshold-like manner: moderate to strong preferential transport was found to occur only for undisturbed soils which contain more than 8 % clay. Preferential flow characteristics were also absent for columns filled with glass beads, clean sands, or sieved soil. No clear effect of land use on the pattern of solute transport could be discerned, probably because the available dataset was too small and too much affected by cross-correlations with experimental conditions. Our results suggest that in developing pedotransfer functions for solute transport properties of soils it is critically important to account for travel distance, lateral observation scale, and water flow rate and saturation. [ABSTRACT FROM AUTHOR]

The current challenge in sustainable agriculture is to introduce new cropping systems to reduce pesticides use in order to reduce ground and surface water contamination. However, it is difficult to carry out in situ experiments to assess the environmental impacts of pesticide use for all possible combinations of climate, crop, and soils; therefore, in silico tools are necessary. The objective of this work was to assess pesticides leaching in cropping systems coupling the performances of a crop model (STICS) and of a pesticide fate model (MACRO). STICS-MACRO has the advantage of being able to simulate pesticides fate in complex cropping systems and to consider some agricultural practices such as fertilization, mulch, or crop residues management, which cannot be accounted for with MACRO. The performance of STICS-MACRO was tested, without calibration, from measurements done in two French experimental sites with contrasted soil and climate properties. The prediction of water percolation and pesticides concentrations with STICS-MACRO was satisfactory, but it varied with the pedoclimatic context. The performance of STICS-MACRO was shown to be similar or better than that of MACRO. The improvement of the simulation of crop growth allowed better estimate of crop transpiration therefore of water balance. It also allowed better estimate of pesticide interception by the crop which was found to be crucial for the prediction of pesticides concentrations in water. STICS-MACRO is a new promising tool to improve the assessment of the environmental risks of pesticides used in cropping systems.

STICS-MACRO is a process-based model simulating the fate of pesticides in the soil-plant system as a function of agricultural practices and pedoclimatic conditions. The objective of this work was to evaluate the influence of crop management practices on water and pesticide flows in contrasted environmental conditions. We used the Morris screening sensitivity analysis method to identify the most influential cropping practices. Crop residues management and tillage practices were shown to have strong effects on water percolation and pesticide leaching. In particular, the amount of organic residues added to soil was found to be the most influential input. The presence of a mulch could increase soil water content so water percolation and pesticide leaching. Conventional tillage was also found to decrease pesticide leaching, compared to no-till, which is consistent with many field observations. The effects of the soil, crop and climate conditions tested in this work were less important than those of cropping practices. STICS-MACRO allows an ex ante evaluation of cropping systems and agricultural practices, and of the related pesticides environmental impacts., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Climate change is not only likely to improve conditions for crop production in Sweden, but also to increase weed pressure and the need for herbicides. This study aimed at assessing and contrasting the direct and indirect effects of climate change on herbicide leaching to groundwater in a major crop production region in south-west Sweden with the help of the regional pesticide fate and transport model MACRO-SE. We simulated 37 out of the 41 herbicides that are currently approved for use in Sweden on eight major crop types for the 24 most common soil types in the region. The results were aggregated accounting for the fractional coverage of the crop and the area sprayed with a particular herbicide. For simulations of the future, we used projections of five different climate models as model driving data and assessed three different future scenarios: (A) only changes in climate, (B) changes in climate and land-use (altered crop distribution), and (C) changes in climate, land-use, and an increase in herbicide use. The model successfully distinguished between leachable and non-leachable compounds (88% correctly classified) in a qualitative comparison against regional-scale monitoring data. Leaching was dominated by only a few herbicides and crops under current climate and agronomic conditions. The model simulations suggest that the direct effects of an increase in temperature, which enhances degradation, and precipitation which promotes leaching, cancel each other at a regional scale, resulting in a slight decrease in leachate concentrations in a future climate. However, the area at risk of groundwater contamination doubled when indirect effects of changes in land-use and herbicide use, were considered. We therefore concluded that it is important to consider the indirect effects of climate change alongside the direct effects and that effective mitigation strategies and strict regulation are required to secure future (drinking) water resources., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Background: Sorption coefficients (the linear K(D) or the non-linear K(F) and N(F)) are critical parameters in models of pesticide transport to groundwater or surface water. In this work, a dataset of isoproturon sorption coefficients and corresponding soil properties (264 K(D) and 55 K(F)) was compiled, and pedotransfer functions were built for predicting isoproturon sorption in soils and vadose zone materials. These were benchmarked against various other prediction methods., Results: The results show that the organic carbon content (OC) and pH are the two main soil properties influencing isoproturon K(D) . The pedotransfer function is K(D) = 1.7822 + 0.0162 OC(1.5) - 0.1958 pH (K(D) in L kg(-1) and OC in g kg(-1)). For low-OC soils (OC < 6.15 g kg(-1)), clay and pH are most influential. The pedotransfer function is then K(D) = 0.9980 + 0.0002 clay - 0.0990 pH (clay in g kg(-1)). Benchmarking K(D) estimations showed that functions calibrated on more specific subsets of the data perform better on these subsets than functions calibrated on larger subsets., Conclusion: Predicting isoproturon sorption in soils in unsampled locations should rely, whenever possible, and by order of preference, on (a) site- or soil-specific pedotransfer functions, (b) pedotransfer functions calibrated on a large dataset, (c) K(OC) values calculated on a large dataset or (d) K(OC) values taken from existing pesticide properties databases., (Copyright © 2011 Society of Chemical Industry.)

Currently, no general guidance is available on suitable approaches for dealing with spatial variation in the first-order pesticide degradation rate constant k even though it is a very sensitive parameter and often highly variable at the field, catchment, and regional scales. Supported by some mechanistic reasoning, we propose a simple general modeling approach to predict k from the sorption constant, which reflects bioavailability, and easily measurable surrogate variables for microbial biomass/activity (organic carbon and clay contents). The soil depth was also explicitly included as an additional predictor variable. This approach was tested in a meta-analysis of available literature data using bootstrapped partial least-squares regression. It explained 73% of the variation in k for the 19 pesticide-study combinations (n = 212) in the database. When 4 of the 19 pesticide-study combinations were excluded (n = 169), the approach explained 80% of the variation in the degradation rate constant. We conclude that the approach shows promise as an effective way to account for the effects of bioavailability and microbial activity on microbial pesticide degradation in large-scale model applications.