Preferential flow modelling of chlorpyrifos leaching in two arid soils of irrigated agricultural production areas in Argentine Patagonia

Measurement and modelling of water flows and pesticide leaching under low input cropping systems

One current challenge in sustainable agriculture is to redesign cropping systems to reduce the use and impacts of pesticides, and by doing so protect the environment, in particular groundwater, and human health. As a large range of systems could be explored and a wide number of pesticides used, field experiments cannot be carried out to study the sustainability of each of them. Thus, the objectives of this work were (1) to measure water flows and pesticide leaching in six contrasted low input cropping systems based on sunflower-wheat rotation, oilseed rape-wheat-barley rotation, and maize monoculture, experimented for three years in three different soil and climatic conditions, and (2) to assess and to compare the ability of three pesticide fate models (MACRO, PEARL, PRZM) to simulate the observed water flows and pesticide concentrations. The systems were designed using various crop rotations, including cover crops and intercrops. The models were parameterized with generic parameter estimation routines as done for regulatory risk assessment, and a method was developed to parameterize intercrops, not represented in the models: the use of average crop factors, maximum LAI, crop height and rooting depth of the crops constituting the intercrop allowed acceptable simulations of cumulative water flows, but not their dynamic. Twelve pesticides of 70 applied were quantified in lysimeter samples (e.g. bentazone, glyphosate, S-metolachlor), and their concentrations exceeded 0.1 μg L-1 in several occasions. The performance of the models to reproduce pesticide concentrations was generally poor illustrating the great challenge and the progress needed to simulate accurately pesticide transfers into the soil. The best fits to measured data were attained using "worst-case" pesticide sorption and degradation parameters. Overall, MACRO performed better than PEARL and PRZM. The method developed to parameterize intercrops could be used for risk assessment of groundwater contamination by pesticides in low input cropping systems, but the use of the three models without any calibration is likely to underestimate pesticide leaching in several situations.

Remote Sensing, Geophysics, and Modeling to Support Precision Agriculture—Part 1: Soil Applications

Sustainable agriculture management typically requires detailed characterization of physical, chemical, and biological aspects of soil properties. These properties are essential for agriculture and should be determined before any decision for crop type selection and cultivation practices. Moreover, the implementation of soil characterization at the beginning could avoid unsustainable soil management that might lead to gradual soil degradation. This is the only way to develop appropriate agricultural practices that will ensure the necessary soil treatment in an accurate and targeted way. Remote sensing and geophysical surveys have great opportunities to characterize agronomic soil attributes non-invasively and efficiently from point to field scale. Remote sensing can provide information about the soil surface (or even a few centimeters below), while near-surface geophysics can characterize the subsoil. Results from the methods mentioned above can be used as an input model for soil and/or soil/water interaction modeling. The soil modeling can offer a better explanation of complex physicochemical processes in the vadose zone. Considering their potential to support sustainable agriculture in the future, this paper aims to explore different methods and approaches, such as the applications of remote sensing, geophysics, and modeling in soil studies.

Temporal Variability in the Response of a Linear Time-Invariant Catchment System to a Non-Stationary Inflow Concentration Field

Predicting the effects of changes in dissolved input concentration on the variability of discharge concentration at the outlet of the catchment is essential to improve our ability to address the problem of surface water quality. The goal of this study is therefore dedicated to the stochastic quantification of temporal variability of concentration fields in outflow from a catchment system that exhibits linearity and time invariance. A convolution integral is used to determine the output of a linear time-invariant system from knowledge of the input and the transfer function. This work considers that the nonstationary input concentration time series of an inert solute to the catchment system can be characterized completely by the Langevin equation. The closed-form expressions for the variances of inflow and outflow concentrations at the catchment scale are derived using the Fourier–Stieltjes representation approach. The variance is viewed as an index of temporal variability. The closed-form expressions therefore allow to evaluate the impacts of the controlling parameters on the temporal variability of outflow concentration.