Modeling the evolution of soil structural pore space in agricultural soils following tillage

doi:10.1016/j.geoderma.2019.07.017

Geoderma

Volume 353, 1 November 2019, Pages 401-414

https://doi.org/10.1016/j.geoderma.2019.07.017 Get rights and content

Highlights

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Temporal dynamics of soil structure are often not included in hydrological modeling.
•
We predicted the dynamics of soil pore size distribution following tillage.
•
The model captured well the post-tillage evolution of soil pore size distribution.
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Lack of adequate data sets are the main limitation for model application.

Abstract

Surface soil structure and pore space are very responsive to both natural and anthropogenic impacts such as rainfall or tillage. These changes affect the soil hydraulic properties as well as the soil water budget. Despite available theories and evidence on the nature of these changes, efforts to capture the temporal dynamics of soil pore size distribution (PSD) and incorporate the derived hydraulic properties in modeling studies are quite rare. The objective of this paper is to examine the suitability of an existing pore evolution model to capture the evolution of soil PSD using water retention parameter (WRP) data sets from around the world. The physical processes governing the evolution of soil PSD are incorporated in the coefficients of the pore evolution model. The applicability of the model to predict the evolution of PSD is discussed and evaluated for two cases (1) when there is a change in tillage regime, and (2) as a novel undertaking, for the months following tillage operations. For the second case, the model is also evaluated for the assumption that the WRPs for the intermediate stages following tillage is not known. This enables us to predict the effects of tillage on soil PSD at a given time even without WRP measurements at all stages. Overall, it is seen that the model and its coefficients are adequate in estimating the overall reduction in porosity and loss of inter-aggregate pores (corresponding to pressure head range from 0 to 330 cm) that are characteristic after tillage operations for both scenarios. In most cases, there is a good agreement between the observed and predicted values indicated by the r² and RMSE values. The model seems to be less suitable for pores with radii ≤10 μm in some cases, especially for intense rainfall scenarios which may lead to rapid aggregate breakdown and formation of finer pores at a faster rate in comparison to moderate rainfall events. As a solution, we may provide more recent initial conditions for initial PSD to the model following heavy rainfall events and continue our simulations from there to better capture the effects of rainfall. The main limitation for the application of the model is, however, the lack of adequate datasets to validate and calibrate it for different management practices, soil types, and climate regimes.

Introduction

Agricultural management practices (AMPs) such as tillage, crop rotation, and cover crops aim to create optimum characteristics for crop growth and minimize environmental degradation by improving soil structural properties, and thus soil pore space. Any disturbance to the surface soil layer (0–30 cm), either anthropogenic (such as AMPs) or natural (such as rainfall), leads to changes in soil structure with consequences for the soil pore space. While the textural or intra-aggregate pores in agricultural soils are not expected to vary over time, the structural or inter-aggregate pores, which drain at pressure heads (h) between 0 and 330 cm (corresponding to pore sizes ≥5 μm according to the Young-Laplace Equation), are most affected by AMPs and natural stresses (Leij et al., 2002a; Bormann and Klaassen, 2008). Large pores created by tillage are usually unstable and subject to compaction (Keller et al., 2017) while the pore size distribution (PSD) changes with time due to rainfall, biological activity, wetting-drying and freeze-thaw cycles as well as when there is a change in the tillage regime (e.g., a regime shift from tilled to no-tilled soil). These changes consequently affect the soil hydraulic properties (SHP), i.e., the water retention function θ(h) and the hydraulic conductivity function K(h), as well as soil water budget components.

Recently, Vogel et al. (2018) emphasized the relevance of considering the temporal dynamics of management induced changes in soil structure. The authors identified a number of relevant process interactions that need to be coupled in order to evaluate the impact of such management practices on water fluxes. One of the main recommendations of their research highlights the need for data over longer time-scales for the validation of modeling approaches to understand the behavior of the system (soil). However, the existence of such data are rare and hampers the assessment of models and their validation as well as adoption to dynamic boundary conditions. Recent studies such as Castellini et al. (2019) and Kreiselmeier et al. (2019) have made efforts in this direction by studying changes in soil physical status under long-term experiments for different crop management practices. The studies further illustrated the need for observing temporal dynamics in soil structure either through experimental quantification (Kreiselmeier et al., 2019) or by identifying indicators like relative field capacity that highlight changes in soil status (Castellini et al., 2019). Predicting the temporal dynamics of SHP and PSD are highly relevant for the supply of water and nutrients to crops and knowledge on them may help us develop tools for sustainable food production and integrated use of natural resources (Lal, 2009; Castellini et al., 2014; Di Prima et al., 2018; Martín et al., 2017).

On a similar note, Chandrasekhar et al. (2018) reviewed the temporal dynamics of SHP due to AMPs and climate influences and reported that most hydrological modeling studies assume constant SHP over time despite evidence that soil structure is subject to temporal variations (see also Castellini et al., 2014; Green et al., 2003; Strudley et al., 2008). Further, the authors evaluated the pore evolution model of Or et al. (2000) and Leij et al., 2002a, Leij et al., 2002b to capture the complex dynamics of soil PSD when there is a shift in tillage regimes as well as for the evolution of PSD through a season. The initial results show that the model was able to capture the temporal dynamics of soil PSD for both cases. However, the study only looks at two case studies which may be insufficient to calibrate the model coefficients as well as to evaluate its suitability under different AMPs at different locations as well as under different climate conditions. Currently, the model has also been applied for soil hydraulic data subject to a sequence of wetting and drying cycles (Leij et al., 2002a) and to analyze if changes in PSD due to different root types can be described by the model (Bodner et al., 2014). However, these are very limited efforts, and they have not been validated over a wide range of soil types, locations, and AMPs in a systematic manner. The evolution of soil PSD through a season following tillage have also been observed (e.g., Kreiselmeier et al. (2019); Sandin et al. (2017)) but are very rarely included in modeling studies (Schwen et al., 2011).

Based on the above assertions, this article builds on the results and limitations put forward by Chandrasekhar et al. (2018). We investigate the applicability of the pore space evolution model to retention data obtained from different tillage treatments from around the world. The model was applied to the datasets for two cases: (1) when there is a change in tillage regime and, (2) for the temporal dynamics of soil pore space in the months following tillage. For these two cases, the water retention parameters (WRP) were known for all time steps, i.e., the initial, intermediate and final stages following tillage. For the second case, we also investigated the suitability of the model under the assumption that the WRP for the intermediate stages were not known to examine the potential of the model to capture the temporal dynamics in PSD with limited measurement campaigns.

Section snippets

Mathematical model

Or et al. (2000) used the following general partial differential equation also known as the Fokker-Planck equation (FPE) to describe the evolution of soil PSD following tillage: $\frac{\partial f}{\partial t} = \frac{\partial}{\partial r} (D (r, t) \frac{\partial f}{\partial r}) - \frac{\partial}{\partial r} (V (r, t) f) - M (t) f$ where f is the PSD or frequency [L⁻¹] of pores as a function of time t [T] and pore radius r [L], D the dispersion coefficient [L² T⁻¹], V the drift coefficient [LT⁻¹] and M the degradation coefficient [T⁻¹]. D and V quantify the changes with time of the variance of the PSD and mean pore

Results

The analytical solution (Eq. (16)) was used to predict the evolution of PSD due to a change in the tillage regime as well as for the months following tillage. The resulting PSD curves are presented in 3.1 Evolution of soil PSD: change in tillage regime, 3.2 Evolution of soil PSD: Following tillage through a season and Fig. 1, Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7, Fig. 8. For these figures, different shades of gray in the background of the PSD curves denote functional pore radii (r)

Discussion

The results show, in general, that the pore evolution model predicts the evolution of PSD for all the cases described in 3.1 Evolution of soil PSD: change in tillage regime, 3.2 Evolution of soil PSD: Following tillage through a season, 3.3 Evolution of soil PSD: knowledge of WRP only for the initial and final steps following tillage very well. The r² values of most of the studies are well above 0.85, and the RMSE values are quite low. The evolution of the larger or inter-aggregate pores,

Summary and conclusion

The dynamics of soil structure and pore space in the topsoil layers in agricultural soils pose a challenge to the characterization of SHP and to their incorporation into mechanistic hydrological models. The pore evolution model by Or et al. (2000) takes into account the changes in mean, the variance of pore radii as well as the instantaneous collapse of large pores following tillage by means of the drift, dispersion and degradation coefficients. The coefficients were quantified using previously

Acknowledgements

Our gratitude goes to Atiqah Fairuz Salleh for her editorial input towards improving the manuscript. We would also like to thank the two anonymous reviewers whose suggestions made the manuscript significantly better.

Funding

This work was supported by the German Research Foundation (DFG) [grant number SCHW 1448/6-1, FE 504/11-1]; and the Austrian Science Fund (FWF) [grant number I-2122 B16].

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Modeling the evolution of soil structural pore space in agricultural soils following tillage

Highlights

Abstract

Introduction

Section snippets

Mathematical model

Results

Discussion

Summary and conclusion

Acknowledgements

Funding

Geoderma

Geoderma

Geoderma

J. Hydrol.

Geoderma

Soil Tillage Res.

Soil Tillage Res.

Soil Tillage Res.

Soil Tillage Res.

Geoderma

Soil Tillage Res.

Geoderma

Soil Tillage Res.

Geoderma

Agric. Water Manag.

Soil Tillage Res.

The Lognormal Distribution

Coarse and fine root plants affect pore size distributions differently

Plant Soil

Temporal changes of soil physical quality under two residue management systems

Soil Use Manag.