Local modeling

Modeling mulch to understand agricultural soils

Source: Water resources research

Ensuring adequate access to water is a major concern for farmers. Crops can underperform or even die in the presence of too little or too much water. But the soil’s ability to hold water is a complex process that depends on variations in soil composition, surface morphology, local temperature, humidity, and wind, among other factors.

Wang et al. seek to model this process for a common agricultural scenario: soil in the presence of residue mulch, remains of a killed winter cover crop. Mulch is known to have several stabilizing effects on the soil, including isolating sunlight, reducing the speed of water flowing over the surface, and minimizing temperature variations.

The authors rely on a finite element method which divides the region in question into a series of discrete layers. At the top is the interface between mulch and air. Below this layer is a series of mulch layers and at the bottom is the mulch-soil boundary. Various inputs are then propagated through the layers according to their governing equations. Precipitation, for example, first arrives from the atmosphere and is regularly absorbed as it passes through each layer.

These physical processes are integrated into a MAIZSIM model, which provides additional biological processes, such as plant growth and the effects of this growth on the soil. Although MAIZSIM models the growth of corn, other crop models sharing the same soil code extend these techniques to other important crops, such as soybeans and potatoes. The authors then complete the extended MAIZSIM model with existing models to simulate the decomposition of the mulch and the exchange of carbon and nitrogen with the soil.

The authors performed two simulations to demonstrate that the model produces plausible results in response to events such as thunderstorms and decay. They found that it responds reasonably in a qualitative sense. However, to produce precise quantitative results, future work will need to calibrate the different modules of the model using data collected in the field. (Water resources research, https://doi.org/10.1029/2021WR030431, 2021)

—Morgan Rehnberg, science writer

Quote: Rehnberg, M. (2021), Modeling mulch to understand agricultural soils, Éos, 102 years old, https://doi.org/10.1029/2021EO210686. Posted on December 22, 2021.
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