Mulch representation in soil-crop models

Abstract

Organic mulching is widely used in agriculture to improve soil conditions and enhance crop performance. However, mulch induces complex changes in soil processes, making process-based soil-crop models essential for understanding and predicting these effects. This non-systematic review summarizes the effects of mulch on soil hydrology, fertility, and temperature to evaluate how these processes are integrated into process-based soil-crop models. Current modeling approaches range from simple surface-residue representations that affect evaporation to more integrated frameworks that simulate decomposition and energy-balance feedbacks. Nevertheless, comprehensive and transferable representations of mulch effects remain limited. Future progress will require open-access datasets and modeling frameworks that explicitly represent mulch processes and feedback mechanisms with soil and crops. These improvements would enhance the implementation and optimization of mulch management strategies under varying environmental conditions.

1 What is organic mulching?

In natural ecosystems, plants influence the environment through the accumulation of litter, which stimulates soil life and strengthens ecosystem nutrient cycling (Veen et al., 2019). In agriculture, most plant material is typically harvested, thus limiting the input of organic matter into the agroecosystem. Mimicking natural ecosystems in an agronomical context by applying undecomposed plant material, i.e., mulch, at least partially, compensates for crop residue removal. In this paper, the term ‘mulch’ refers specifically to organic mulching (using undecomposed plant-based litter) as opposed to plastic, inorganic, or living mulches.

Organic mulch is primarily used to improve the crop’s yield and water-use efficiency (WUE) (Qin et al., 2015; Li et al., 2018; Yu et al., 2018). A meta-analysis across 19 countries reported a 20% increase in yield and WUE for wheat and maize under straw mulching, though outcomes vary with environment, crop type, nitrogen and water availability (Qin et al., 2015). Moreover, when matched to crop, management, and climate, mulch has been reported to buffer soil temperatures, support microbial activity and enhance nutrient cycling (Iqbal et al., 2020; El-Beltagi et al., 2022; Ullah et al., 2022). It has also been reported to reduce residual pesticides, fertilizers, heavy metals, while suppressing weeds (Iqbal et al., 2020), and enhance disease tolerance (Kisinga et al., 2026). However, the limited availability of local biomass or its conflict with other uses, and the labor-intensive nature and high transportation costs of its application are challenges to its broader adoption (El-Beltagi et al., 2022; FAO, 2021).

A wide variety of organic mulch types have been used in agriculture, each with its own advantages in specific locations. The degree to which these mulches affect the soil quality, defined in this article as the soil’s capacity to function and support plant productivity (Karlen et al., 1997), depends on mulch type, thickness, and application method (e.g., full or partial cover and ridge–furrow systems) (Kader et al., 2017; El-Beltagi et al., 2022; Souza et al., 2022). Kader et al. (2017) have provided a broad overview of application methods and mulch types. Readers are referred to this work for more in-depth information.

Although organic mulching is promoted as a valuable management strategy, its mechanisms and representation in soil-crop models remain insufficiently understood. Improving this representation is essential for evaluating mulch management strategies and supporting decision-making under varying environmental conditions. In this non-systematic review paper, we introduce the effects of organic mulching on soil hydrological processes, soil fertility, and soil temperature, focusing on mechanisms represented in process-based soil-crop models (Figure 1). This information is then used to evaluate how commonly process-based soil–crop models represent mulching, and to discuss their limitations and potential.

Figure 1

2 Effects of organic mulching on soil quality

2.1 Effects on soil hydrological processes

Runoff is the first pathway of water loss. Mulch protects the soil surface by dissipating raindrop kinetic energy, reducing particle detachment and flow velocity, and thereby limiting erosion and runoff (Mulumba and Lal, 2008; Fan et al., 2023; Blanchy et al., 2023).

Mulches also increase soil water infiltration by limiting crust formation (Fan et al., 2023; Blanchy et al., 2023; Rossi et al., 2024) and increasing soil organic matter (SOM), which promotes soil aggregates and soil porosity (Jordán et al., 2010; Hu et al., 2026; Rossi et al., 2024). This improved soil structure further enhances soil water retention capacity (Mulumba and Lal, 2008; Jordán et al., 2010; Blanchy et al., 2023; Hu et al., 2026). Although mulches intercept and absorb part of the rainfall (Lopes et al., 2025), field studies generally report improved soil water storage (Cook et al., 2006).

Finally, by reducing solar radiation reaching the soil, mulching lowers soil evaporation and helps conserve moisture (Price et al., 1998; Ranaivoson et al., 2017; Blanchy et al., 2023; Dou et al., 2024). Over time, the application of mulch tends to improve the water budget for crops (Jordán et al., 2010; Dou et al., 2024).

2.2 Effects on soil fertility

Mulch influences soil fertility, defined as the soil’s ability to sustain plant growth by nutrient availability and biological activity (AGROVOC, 2021). Its effects on hydrological processes play a primary role, as nutrient transport is linked to transpiration, and drought can limit nutrient uptake despite the availability of nutrients (Woodward, 1699; Cramer et al., 2008).

During its decomposition, mulch serves as a nutrient source, primarily for nitrogen (N). Overall, it improves crop N use efficiency (Qin et al., 2015) and reduces N and phosphate loss (Döring et al., 2005; Bakr et al., 2015; Liu et al., 2021; Du et al., 2026). However, benefits depend on the synchronization of nutrient release with crop demand (Zhang et al., 2023). In this context, mulch quality in terms of C:N ratio is a key determinant of mineralization rate and the associated immobilization of soil nutrients (Cattanio et al., 2008; Truong and Marschner, 2020). Low C:N ratio mulches (e.g, leguminous residues) decompose faster, stimulate microbial mineralization, and enhance soil N availability, whereas high C:N ratio mulches (e.g, woody residues) immobilize N and reduce crop production (Gentile et al., 2011; Palm et al., 2014; Yan et al., 2018; Truong and Marschner, 2020). The form of organic C also matters. Labile compounds, such as glucose cause stronger immobilization than more stable forms such as cellulose or tannins (Cao et al., 2021).

By buffering moisture and providing carbon and nutrients, mulch stimulates microbial activity and accelerates residue turnover, enhancing nutrient cycling (Tu et al., 2006; Wang et al., 2019; Du et al., 2026; Mahawar et al., 2026) and improving SOM content (Palm et al., 2014; Sokol et al., 2022).

In summary, mulch can improve fertility by supplying nutrients and carbon and by maintaining soil moisture that supports microbial activity. The net outcome depends on mulch quality (e.g., C:N ratio and carbon form) and on how decomposition-driven nutrient release aligns with crop demand. Projected climate change will accelerate decomposition (Mori et al., 2020), underscoring the importance of mulching on soil fertility.

2.3 Effects on soil temperature

Soil temperature is correlated with air temperature and surface cover (Amato and Giménez, 2024) and governs the rate of various physical, chemical, and biological processes such as microbial activity, seed germination, plant development, pesticide degradation, and organic matter decomposition (Jury et al., 1991; Hillel, 1998). Mulch-driven changes in soil temperature have been observed to affect shoot growth, root development, biomass allocation, and nutrient uptake (Gavito et al., 2001).

Primarily, mulch alters surface energy fluxes by reflecting and absorbing shortwave radiation (Chung and Horton, 1987; Sui et al., 1992; Price et al., 1998; Enrique et al., 1999), and by changing how energy is partitioned between latent and sensible heat during wetting and drying cycles (Bristow and Horton, 1996; Price et al., 1998; Novak et al., 2000). In addition, a mulch layer acts as a thermal conductivity barrier, reducing the soil’s heat loss (Larsson and Båth, 1996; Komariah et al., 2008; Shirisha et al., 2023). As a result, mulching buffers daily soil temperature fluctuations, cooling soils in summer and keeping them warm in winter, though outcomes depend on mulch thickness, mulch type, soil type, and climate (Larsson and Båth, 1996; Price et al., 1998; Zhang et al., 2009; Nazemi Rafi and Kazemi, 2021; Shirisha et al., 2023).

The effects of mulch-induced soil temperature changes on crop development depend on local climate, vary seasonally (Sekhon et al., 2005), and are governed by crop-specific thermal requirements (Qin et al., 2015; Kader et al., 2017). While a buffered soil temperature can improve and accelerate plant development under certain circumstances (Sekhon et al., 2005; Kosterna, 2014), it can reduce yields or delay plant development under other circumstances (Crutchfield, 1984; Fortin and Pierce, 1990; Yang et al., 2006; Chen et al., 2007).

3 Representation of mulching in process-based soil-crop models

Mulching affects multiple interacting processes (Figure 1), making its impacts context-dependent. Process-based soil–crop models address this by representing soil–crop dynamics and interactions to simulate complex outcomes such as yield and soil water balance (Roose and Schnepf, 2008). These models are widely used for decision support in crop management, studying genotype–environment interactions, and climate impact analysis. Consequently, process-based models can assess the effects of mulch when key processes are represented (Kader et al., 2019). However, many models lack explicit mulch modules or have not been tested for this purpose.

3.1 General simulated soil processes

Models can be categorized into four distinct types according to the processes they represent. In this model, mulch is typically represented as an additional interacting component with specific properties.

Type 1 models only include the direct hydraulic impact of a mulch layer. Most commonly, this effect is implemented by reducing soil evaporation and runoff. For example, a mulch layer can reduce soil evaporation by acting as a sponge. It absorbs water and evaporates the intercepted water before the soil water is depleted (Porter et al., 2010). Surface runoff, on the other hand, can be reduced by increasing simulated surface roughness, which promotes temporary ponding and enhances the infiltration rate (Brisson et al., 2003; Porter et al., 2010). These processes are often simplified using a “bucket” approach, in which mulch is represented as an intercepting reservoir with a fixed storage capacity (Gusev et al., 2018; Irkiso et al., 2025). Overall, this representation improves simulated plant water availability.

Type 2 models expand on Type 1 by incorporating mulch effects on the soil nutrient transformation and transport processes. Soil nutrient dynamics are generally represented by mass-balance equations. In these equations, nutrient pools change according to process-based rates influenced by soil temperature, soil moisture, and soil pH (Boote, 2019; Chaves et al., 2024). Therefore, mulch is represented via the mass balances and process rates governing the simulated system (Findeling et al., 2007; Boote, 2019). Nutrient availability influences root uptake, crop growth, and, indirectly, transpiration and soil water dynamics through feedbacks between biomass production and water demand (Probert et al., 2005).

Type 3 models also build on Type 1 by incorporating the effect of mulch on the system’s energy balance rather than on the nutrient cycle. Soil temperature is commonly simulated using a heat-transport equation (Jones et al., 2003; Kroes et al., 2017), with mulch effects implemented through modified surface energy balance terms (Scopel et al., 2004).

Type 4 models combine all three major connections of Type 1, 2, and 3 models: (i) the effect on water relations, (ii) the nutrient cycle, and (iii) the energy balance. These models simultaneously simulate water, nutrient, and energy balances and their effects on crop growth, including interactions such as the influence of soil temperature on water and nutrient availability (Sharma and Bhardwaj, 2017). While this is a common feature in soil-crop models, and Type 4 can be used to model all connections between mulch and the soil-crop ecosystem (e.g., Chaves et al., 2024), in practice, Type 4 is rarely tested due to its complex parametrization and the limited availability of datasets for model evaluation (Scopel et al., 2004; Balwinder-Singh et al., 2011; Mupangwa et al., 2011; Kodešová et al., 2014; Kisaka et al., 2016; Fraga and Santos, 2018; Kader et al., 2019; Dusserre et al., 2020; Ahirwar et al., 2023; Amin et al., 2023; Irkiso et al., 2025) (Table 1).

In addition, soil biotic processes are not well represented in soil-crop models, even though they play an important role in soil quality (see section 2.2).

3.2 Common process-based soil-crop models

Numerous process-based soil-crop models have been developed over time, but their ability to simulate the effects of mulch varies. Here, we discuss six commonly used soil-crop models and their ability to represent mulching. These models are categorized according to the classification described above. APSIM, Aquacrop, DSSAT-CSM, HYDRUS 1-D, SWAP-WOFOST, and STICS. Table 1 summarizes the main characteristics and mulch-related functionalities. It is important to note that this list is not exhaustive, and other models can also be adapted to represent mulch as well, e.g., ARMOSA (Tadiello et al., 2023), PASTIS (Findeling et al., 2007), RothC (Pesce et al., 2024), and nameless models (Souza et al., 2022).

APSIM (Agricultural Production Systems Simulator) (McCown et al., 1996) can simulate systems for a diverse range of plant, animal, soil, climate, and management interactions, including organic mulch. The SurfaceOM module calculates the addition and decomposition of organic material at the soil surface, influencing soil evaporation and runoff. Carbon and nitrogen cycling are represented by complementary modules: SoilN simulates belowground transformations, while RESIDUE describes aboveground residue dynamics. Indirect effects of mulch, such as increased biomass production and associated changes in transpiration, are incorporated through SoilN processes (Probert et al., 2005). However, APSIM can not reproduce temperature-induced delays in crop phenology, because it does not account for canopy temperature or mulch-driven soil temperature effects on crop growth (Balwinder-Singh et al., 2011, 2016; Gaydon et al., 2017). To address this limitation, Gaydon et al. (2017) modified the temperature-time representation in the model.

AquaCrop simulates crop yield responses to water and is specifically developed where water is a major limiting factor for productivity (Steduto et al., 2009). The model is designed to be simple, robust, and accurate, requiring relatively few parameters and input variables. In AquaCrop, mulch only affects soil evaporation through two characteristics: soil cover fraction (0-100%) and mulch type. While this simplicity enables rapid assessment of mulch effects on soil–crop water relations, the model does not account for nutrient and energy balances or their interactions. Therefore, AquaCrop is useful when these processes are negligible or outside the research scope, while avoiding overparameterization (Shanono et al., 2022; Umesh et al., 2022; Zizinga et al., 2022). However, while all models are simplifications of reality, in real-world applications, the nutrient and energy balance can rarely be neglected. As such, the use of AquaCrop to model mulch effects should be done with caution and careful consideration of the limitations of the model.

DSSAT (Decision Support System for Agrotechnology Transfer) simulates growth, development, and yield for a range of crops from coupled soil-plant-atmosphere processes (Jones et al., 1998). Adding a mulch layer in DSSAT has a dual effect on soil evaporation: it stores and evaporates intercepted water, and buffers soil evaporation by meeting evaporative demand first. Furthermore, mulch representation promotes temporary ponding and increases infiltration, the latter represented by modified runoff coefficients and water storage limits (Porter et al., 2010). Soil temperature is computed with a solar radiation and albedo-based module (Jones et al., 2003), which can potentially capture mulch effects. With regard to nutrient dynamics, the CENTURY module (Gijsman et al., 2002) incorporates the effects of decomposing surface organic residue layers on soil organic matter and nutrient cycling. This decomposition is influenced by soil temperature and soil water content (Hoogenboom et al., 2019).

Hydrus–1D (Šimůnek et al., 2008) and SWAP (Kroes et al., 2017) are finite element models capable of simulating water flow, as well as heat and solute transport. SWAP (Kroes et al., 2017) can be coupled with WOFOST (Groenendijk et al., 2016) to simulate complex crop growth (Heinen et al., 2024). Neither model includes a built-in option for a simulated mulch layer (Amin et al., 2023). Mulch effects can nevertheless be represented by (i) adding an explicit surface layer with parametrized hydraulic and thermal properties (Kodešová et al., 2014; Kader et al., 2019; Amin et al., 2023), or (ii) using simplified boundary-condition approaches (Estrella Delgado et al., 2026). While mulching effects on nutrient dynamics could, in theory, be captured by calibrating solute-transport parameters in the two aforementioned models, to the best of our knowledge, no studies to date have applied this adaptation.

STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard) simulates the dynamic interactions among crops, soil, climate, and agricultural management modules (Beaudoin et al., 2023) and provides one of the most integrated mulch implementations among the models discussed. A mulch layer directly affects soil evaporation by intercepting precipitation (Scopel et al., 2004) and reducing surface runoff. Water intercepted by the layer evaporates (Brisson et al., 2003), adding moisture to the atmosphere, reducing crop transpiration, and impacting soil temperature. Soil evaporation is further reduced through the energy balance via the interception of solar radiation by the mulch layer (Scopel et al., 2004). Surface runoff is affected by mulch through the presence of soil surface obstacles (Brisson et al., 2003). As such, a mulch layer reduced runoff in proportion to the amount of mulch (t ha^-1) that is applied to the soil, using a threshold of mulch presence above which no runoff occurred (Beaudoin et al., 2023; Scopel et al., 2004). In addition, STICS computes mulch decomposition rates that depend on the type of plant residue. Decay of the mulch, in turn, affects the nutrient cycle by altering soil nitrogen availability (Beaudoin et al., 2023).

4 Challenges and opportunities in mulch modeling

Organic mulches improve soil quality and stabilize crop performance, but their effects are context-dependent and vary with soil type, plant species, mulch type, mulching method, and local climate. Therefore, understanding and predicting mulch effects under varying environmental conditions requires process-based soil–crop models that simulate coupled soil–plant processes and their feedback mechanisms (Figure 1).

While a wide range of models exists, explicit representation of mulch remains limited or absent.

In hard-coded models, mulch is often represented by modelers as an additional surface or adapted soil layer based on field and laboratory measurements (Kodešová et al., 2014; Kader et al., 2019). The development of more flexible and modular open-source modeling frameworks facilitates the integration of a mulch module, as demonstrated by Souza et al. (2022).

A major challenge remains the development of robust and transferable parameterizations of the mulch model. Mainly due to the extended range of mulch effects, contrasting environmental conditions, multiple management strategies, and different model representations. Progress in this area will require well-documented experimental datasets to support parameter estimation, calibration, sensitivity analysis, and independent model validation.

Model selection should also remain consistent and be guided by data availability and study objectives: simpler approaches, such as AquaCrop, may be adequate under limited-data conditions, whereas more complex models, such as STICS, require extensive calibration to reliably represent mulch-driven processes.

Future advances in mulch representation will improve the predictive capacity of soil–crop models and support the implementation and optimization of mulching as a sustainable agricultural management strategy. In particular, improved process representation could help quantify how long mulch-induced changes in nutrient availability persist, how different mulch types modify soil temperature, and how mulching alters crop resilience to drought in terms of timing, duration, and severity. Ultimately, such developments would support the optimization of mulch management strategies for specific crops and environmental conditions.

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