Assessment of Water Retention and Absorption of Organic Mulch Under Simulated Rainfall for Soil and Water Conservation

Abstract

The use of organic mulch as a natural practice to enhance water retention and absorption is underexplored, highlighting the need for a deeper understanding of its effectiveness under varying conditions. The aim of this study was to investigate the process of interception, retention, and absorption of rainwater by different types, sizes, and densities of some organic mulch covers. Six organic mulches of various sizes were used, all largely available in the Brazilian semiarid: coconut leaf (cc), cashew leaf (ca), elephant grass (el), corn leaf (co), Brachiaria grass (br), and sugar cane leaf (su), under simulated rainfall conditions. The experimental scheme consisted of a factorial of six types of mulches, three sizes (50, 100, and 200 mm), and four densities (1, 2, 4, and 8 t ha⁻¹). Water adsorption and retention curves were constructed, and the interception capacity of different vegetation materials was estimated. Analysis of variance, Tukey Test, Regression polynomial, and Principal Components Analysis were applied. It was observed that increasing density systematically led to an increase in water retention and absorption. For 8 t ha⁻¹ the values were 11 to 23% for water retention and 7 to 16% for water absorption of the gross rainfall depth. When comparing 8 t ha⁻¹ and 2 t ha⁻¹ densities, rainfall retention and absorption increased more than 100%. Higher values were obtained for cashew and Brachiaria grass, improving water retention and cashew leaves for absorption. Coconut leaves promoted only 83% retention and 67% water absorption, when compared to the cashew leaf and Brachiaria grass.

1. Introduction

Canopy interception plays an important role in hydrological budgets, controlling runoff generation and soil erosion, particularly in semiarid areas, where plant soil cover is usually limited. Investigations aiming to evaluate the canopy storage capacity at organic mulching surfaces have been increasingly carried out, focusing on the rainfall net contribution to soil moisture [1,2,3].

To evaluate the net rainfall reaching the soil surface, Geddes and Dunkerley [4] emphasize the importance of vegetation residues, which can cover 50% or more of the soil surface and intercept a significant portion of rainfall [5]. Therefore, quantifying the storage capacity of mulch surfaces is essential for a comprehensive understanding of water infiltration processes through the soil.

Mulch cover usually reduces direct raindrops’ impact on soil surfaces (reducing soil disaggregation), minimizing soil nutrient losses, thus contributing to enhance soil fertility. Moreover, mulching contributes to increased soil water availability and infiltration. On the other hand, mulch surfaces tend to reduce soil evaporation and to buffer soil temperature dynamics [6,7,8].

In areas with limited natural soil cover and where soil and water conservation practices are not adopted, high runoff rates and sediment transport are expected to occur, leading to nutrient losses and limiting crop production. Such losses are still high around the world, due to the lack of adoption of conservation practices [9,10,11,12].

Mulching is highly recommended for semiarid areas. Karuku [13], Silva et al. [11], and Montenegro et al. [14] have verified that straw mulching with densities of 2 and 4 t ha⁻¹ not only successfully reduced runoff and soil temperature, but also resulted in higher soil moisture and thus higher crop production. Experimental plots have been considered in an experimental basin in the Brazilian semiarid Pernambuco State, and organic mulching from beans and coconut powder were adopted. Moreover, mulching reduces soil evaporation, thus increasing water availability for crop transpiration [3]. However, it should be highlighted that too high cover densities might reduce the water amounts reaching the soil surface due to interception, limiting soil water availability [15].

Organic mulching might contribute to increased crop production and also to enhance soil and crop quality, particularly in areas under water scarcity and subject to adverse climate change scenarios. Ranjan et al. [16] have evaluated the advantages of organic mulching for increasing the production of fruits and vegetables. For the Brazilian semiarid, climate changes are expected to increase temperatures and reduce rainfall amounts [17]. Hence, conservation practices in rural lands are required for such areas.

Regional studies have revealed the high potential of organic mulching for soil and water conservation, based on vegetation types usually available on their respective locations. Cerdà et al. [18] have studied the long-term performance of 1.25 t ha⁻¹ oat straw being applied yearly in Spain, verifying its strong impact on runoff and soil loss reduction in rainfed agriculture and pasture areas. Keesstra et al. [19] used vegetative cover and pruning residues from the apricot culture itself, having been managed for 20 years, in the river Albaida basin in the north of the province of Valencia in the east of Spain. Lucas-Borja et al. [20] carried out experimentation with the use of straw, at a density of 2 t ha⁻¹ in southwestern Spain. Montenegro et al. [15] used elephant grass straw, with an application rate of 7 t ha⁻¹ and applied it to corn planting in the semiarid region of Pernambuco, Brazil. In the same region and agricultural cultivation, Carvalho et al. [21] used coconut straw, with an application rate of 8 t ha⁻¹, for the rainfed cultivation of corn.

Shen et al. [22] evaluated the effect of different rates of wheat straw mulch (0.6 and 12 t ha⁻¹) on the soil under rainfed conditions during the years 2009 and 2010 in northern China with two varieties of corn, verifying that the adopted cover contributed to the increase in soil moisture to a depth of 0.20–0.80 m. These authors also noted that grain yield was higher with the presence of mulch at the highest application rate (12 t ha⁻¹) compared to other treatments.

In assessing surface hydrological processes, it is recognized that mulch cover initially retains part of the gross rainfall through interception and absorption. Under near-saturation conditions, it then releases excess water to the soil surface. This process resembles throughfall, which occurs when natural vegetation allows raindrops to pass through [11,23].

The initial water storage capacity of mulch cover is crucial because it delays the onset of drainage flow, representing the amount of water needed to fully saturate the mulch [24]. Significant variations in initial flow abstraction can occur depending on the cover’s architecture and density, with greater abstraction observed at higher coverage densities. Lopes and Montenegro [25] noted different initial abstractions by modeling overland flow in experimental plots using the SMAP Model, under varying levels of soil cover in semiarid conditions.

Lima et al. [26] addressed the effect of the size of rice straw mulch applied on the soil surface on runoff and soil loss, conducting experiments using a soil flume and a rainfall simulator, for three sizes of rice straw mulch (10 mm, 30 mm and 200 mm). The experimental results showed that for the same mulch application rate (by weight), the smaller mulch sizes (i.e., higher surface coverage percentage) presented lower soil loss. However, for the tested rice straw application rates, runoff volume decreased with increasing mulch size, mainly because of the differences in the amount of water absorbed by mulch of different sizes.

The higher carbon/nitrogen (C/N) ratio is an important factor to consider when selecting organic mulches, as it influences the decomposition rate and nutrient availability in the soil. Organic materials with a higher C/N ratio decompose more slowly, which can result in longer-lasting mulch coverage and greater soil protection. Additionally, the C/N ratio affects the microbial activity in the soil, influencing nutrient cycling and plant growth. Therefore, explaining the significance of the C/N ratio will provide a better understanding of its role in the selection criteria for the organic mulches used. This will help contextualize the choice of materials and highlight their impact on soil health and crop performance [27].

Despite previous studies on the impact of mulching on hydrological processes, there is still a lack of research that examines rainfall interception for different organic mulching densities, cutting sizes, and types together. Therefore, the objective of this study was to investigate the process of rainwater interception (i.e., retention and absorption) by different types, sizes, and densities of organic mulches commonly found in the Brazilian semiarid region. These findings are important for guiding soil and water conservation practices in these semiarid environments.

2. Materials and Methods

Laboratory experiments were conducted in the Agricultural Machinery Laboratory (8°1′3.06″ S and 34°56′44.69″ W, and altitude of 38 m) of the Agricultural Engineering Department of Federal Rural University of Pernambuco, Brazil (UFRPE) in Recife, Pernambuco state, Brazil, using a rainfall simulator.

2.1. Organic Mulch

The organic mulches used (Figure 1) in this study came from tropical crops and are largely available in the Brazilian semiarid region. Six organic mulches were used in this study: coconut leaf (cc), cashew leaf (ca), elephant grass (el), corn leaf (co), Brachiaria grass (br), and sugar cane leaf (su).

The choice of plant materials based on a higher carbon/nitrogen ratio contributes to lower degradation, helping to maintain cover for longer time at the soil surface. Corn has a ratio of 35.3 [27,28], Brachiaria grass a ratio of 32.5 [29], sugar cane a ratio of 122.5, coconut leaf a ratio of 63.7 [30], elephant grass a ratio of 41.7 [31], and cashew leaf a ratio of 38 [32].

The use of corn, Brachiaria grass, and sugar cane straw as soil cover is already widespread; however, there is no local field experience in using elephant grass, cashew leaves, and coconut leaves. All the coverages adopted have advantages such as high availability, with no associated costs, thus facilitating the use by local farmers.

After harvesting, the plant materials were dried in a forced-air circulation oven at approximately 65 °C. The drying process lasted 48 to 72 h until the dry weight stabilized, after which the materials were cut into three sizes: 50, 100, and 200 mm. Then, by weighing, the quantities needed to obtain densities of 1, 2, 4, and 8 t ha⁻¹ (respectively, masses of 0.113, 0.226, 0.453, 0.907 kg) were prepared to be uniformly spread over the experimental mulch measuring area. The adopted experimental mulch support area was a planar horizontal rectangle with 270 × 420 mm (the largest size was in the direction of oscillation of the emitting nozzle of the rainfall simulator, see Figure 2), allowing the positioning of the mulches, with different sizes and densities, with adequate uniformity.

2.2. Rainfall Simulator

The rainfall simulator used consists of a metal structure (Figure 2) with a free span of 2 m and an oscillating nozzle (VeeJet 80.100—Spraying System^® type —U.S. Corporate Office, Glendale Heights, IL, USA) positioned at a height of 2.5 m. The simulator has a centrifugal pump of 367 W (1/2 CV), working with a positive suction head coupled to a 1 m³ constant head reservoir. At the outlet of the pump there is a valve and a pressure gauge, which allows it to operate at different service pressures, consequently allowing changes in precipitation intensity, uniformity distribution, and drop diameter.

In this set of experiments, the rainfall simulator was adjusted to operate at a constant pressure of 80 KPa for all simulations. The simulator was calibrated in a controlled environment, free from wind currents and direct solar radiation. Mean rainfall intensity was measured using 14 pluviometers distributed around the experimental plot.

A high-precision digital scale was adapted to the mulch support, and the weight was visually measured every 15 s to obtain the water retention and absorption curves. The rainfall duration was 10 min, with a uniform intensity of 90 mm h⁻¹. Weighing continued for an additional 5 min to observe the loss of drainable water. Raindrop sizes were evaluated using the flour pallet methodology, yielding a mean diameter of 3.2 mm, a minimum of 1.9 mm, a maximum of 6.2 mm, and a standard deviation of 0.9 mm. The choice of intensity was based on previous studies that used typical semiarid rainfall with return periods of ~10 years [33,34], which was classified as an extreme rainfall event. The rainfall simulator has been previously calibrated, and the uniformity coefficient was 89%. More details can be found in Montenegro et al. [35].

The water retention curves (Figure 3) are similar to those presented by Niziolomski et al. [36], despite those measurements being carried out under natural conditions.

2.3. Water Retention and Absorption Curves

Absorption capacity was determined based on de Lima et al. [26] throughout the experiment. For time “t”, the absorption capacity can be written as Equation (1).

(1)$Absorption capacity\left(\%\right)={\displaystyle \frac{W_t-W_{dry}}{W_{dry}}}\times 100$

where $W_t$ is weight of wet mulch at time t and $W_{dry}$ is weight of air-dried mulch before the rainfall event.

Additionally, in order to investigate both the interception process during the rainfall event and the water drained out from mulch after the rainfall has stopped, for different mulch densities and sizes, a lumped analysis was carried out considering the mean experimental results for all mulch types together. Then, analysis was performed in order to identify possible typical patterns for the absorption curves.

Instead of adopting a power function (like chosen by Samba et al. [37]), exponential functions with asymptotic behavior were tested, such as Equation (2):

(2)$W_t=c_1(1-e^{-{\left({\displaystyle \frac{t}{a}}\right)}^{\beta }})$

where “$t$” is the time lag, used for analyzing water depths during both the interception period of rainfall occurrence and the drainage period after the rainfall has stopped.

Parameter “$c_1$” is called the sill, “$a$” is the range of the function, and α is a form factor. Journel and Huijbregths [38] called such function exponential (for $\beta$ = 1) or gaussian (for $\beta$ = 2). The parameter α is related to the angle in Figure 3.

The reason for selecting such functionals is due to the nature of both the interception process and the water drainage from canopy, which usually presents an asymptotic behavior as $t$ increases. Hence, Equation (3):

(3)${lim}_{t\to \mathrm{\infty }}W\left(t\right)=c_1$

Then, “$c_1$” will be related either to canopy saturation or the cessation of drainage from the mulch surface, disregarding evaporation from canopy.

2.4. Statistical Analysis

The experimental scheme consisted of a factorial design with 6 types of mulches, as mentioned earlier, including 3 different sizes (50, 100, and 200 mm) and 4 densities (1, 2, 4, and 8 t ha⁻¹). This resulted in a total of 72 experimental mulch cover treatments, with each treatment being replicated three times. An analysis of variance was performed to analyze the data. For significant treatments, a significant means test (Tukey) was conducted at probability levels of 1% and 5%.

The adjustments were evaluated based on the Coefficient of Determination (R²) as well as the Willmott Agreement Index (d). Equation (4):

(4)$d=1-{\displaystyle \frac{\sum _{i=1}^n{(P_i-O_i)}^2}{\sum _{i=1}^n{\left[\left|P_i-\overline{O}\right|+\left|O_i-\overline{O}\right|\right]}^2}}$

The Willmott [39] agreement index (d) is a measure of the degree to which forecasts are error-free, which reflects the degree to which the observed variation is accurately estimated by the simulated variation. It varies between 0.0 and 1.0, where the value of 1.0 indicates perfect agreement. In addition, the performance index (c) [40,41] was calculated, as the product between the correlation coefficient (r) and Willmott index (d), and classified according to Table 1.

An additional statistical procedure employed was Principal Components Analysis (PCA) for the variable’s “size”, “type”, and “density”. PCA uses an orthogonal transformation to convert a set of potentially correlated observations into a set of linearly uncorrelated variables known as principal components. This transformation is designed so that the first principal component has the highest possible variance, with each subsequent component having the highest variance under the constraint of being orthogonal to the preceding components. The covariance matrix can be evaluated, and thus the associated eigenvectors and the eigenvalues of the matrix calculated. Eigenvalues were calculated to assess the contribution of each variable to the total variance of the principal components [42].

3. Results

3.1. General Behavior of Retained and Absorbed Depths

The temporal behavior of the retained and absorbed depths over a 15 min period, with a rainfall duration of 10 min, can be observed in Figure 4 (also refer to the sketch in Figure 3). The behavior follows a standard pattern (as shown in Figure 3), with a higher rate of retention in the initial seconds and then a tendency to stabilize as it approaches 10 min. After the rainfall event ends at 10 min, there is a remaining water depth that is available to drain, which tends to stabilize within 5 min.

3.2. Initial Absorption by Different Mulch Covers

The angles formed between the lines generated by the initial water absorption for each treatment and the x-axis represent the speed at which the cover retains the initial rainfall, denominated initial depth retention angle α (°), as it becomes easier to visualize in Figure 4. The statistical results of the analysis of variance for the different organic mulches used can be observed in Table 2. They showed statistical differences for a 1% significance level when observing the factors of type, size, density, and the interaction between type and size. Significant differences in the initial water retention process were observed among the various mulch covers.

The interaction between type and size (Table 2) revealed that the continuous coverage area is a highly influential factor for water retention on mulch covers. The combinations that promoted the highest initial depth retention were cashew leaf and brachiaria grass, particularly when associated with the smallest sizes (50 mm).

Figure 5 presents the results of the angle of initial water retention. Significant differences were observed on the Tukey test for the type of coverage, with the best-performing materials in resisting rain passage being cashew leaf (CA), elephant grass (EL), and Brachiaria grass (BR) (Figure 5a). Conversely, the poorest rainwater retention conditions were noted for maize leaf (CO) and sugar cane leaf (SU) (Figure 5a). Regarding size, no significant equations were obtained (Figure 5b). However, for density, significance was observed through an exponential equation (Figure 5c), indicating that higher densities promote greater water retention on and within the covers. This highlights the differences among mulch covers and their impact on the timing of surface runoff onset.

3.3. Absorption and Retention by Different Covers

Table 3 displays the analysis of variance for the water retention curve. This table indicates the significance of the isolated factors and their interactions. The obtained probabilities suggest that there are interactions among the factors size, type, and density, and significant differences among mean values occur. Similarly, for the water retention curve, the most effective combinations for absorption were cashew leaf and brachiaria grass, associated with the smallest size (50 mm).

In Figure 6, water retention (after 10 min) and absorption (after 15 min) can be observed, with the same values presented for depth in mm and as a percentage of rainfall for all mulch types. When looking at the mulch types, Brachiaria grass and cashew leaf were the most efficient to retain the largest depths, while for the absorption depths, cashew leaf was superior to Brachiaria grass, differing significantly (Figure 6). The values of the retained and absorbed depths by the coconut leaf were low, being the smallest ones statistically (Figure 6).

In Figure 7, water retention (after 10 min) and absorption (after 15 min) can be observed, with the same values presented for depth in mm and as a percentage of rainfall for all mulch sizes. When assessing the impact of the size factor of the mulch covers, it appears that although there is a significant difference (Table 2), the linear equation does properly represent (at 5%) the variations in the mean retained or absorbed depth as a function of the size values of 50, 100, and 200 mm (Figure 7).

Figure 8 presents the retained and absorbed depths as a function of mulch density. The data fits well to the second-order polynomial equation, with significance at a 1% probability level. It is also observed that an 8-fold increase in density led to an approximately 2.5-fold increase in absorption.

For an application density of 8 t ha⁻¹, the values were 11–23% and 7–16% of the rainfall depth, respectively, under the studied conditions. When comparing 8 t ha⁻¹ with 2 t ha⁻¹, rainfall retention and absorption increased by more than 100%.

Cashew leaf and Brachiaria grass showed better performance in terms of water retention and absorption, respectively. Coconut leaves, when compared to cashew leaf and Brachiaria grass, only retained 83% and absorbed 67% of the water. Smaller sizes of organic mulches were associated with higher water retentions. For a retained depth of 50 mm, the percentages varied from 7 to 23% of the rainfall, while for a depth of 200 mm, it varied from 5 to 18%. The percentages for absorbed depth were 4–18% and 2–14%, respectively.

3.4. Drained Seepage Depth by Different Covers

It can be seen in Table 4 that the drained seepage depth (∆ value represented in Figure 3) presented statistical differences when observing the factors type, size, density, and the interaction between type and size. The local evaporative capacity was neglected due to the indoors simulation and the short exposure time of the experiment. Table 4 shows the significance of the isolated factors, as well as their interactions. According to obtained probabilities, it can be verified that there are interactions among the factors size, type, and density, and significant difference among mean values occur.

In Figure 9, the difference between water retention (after 10 min) and absorption (after 15 min) depths can be observed as a function of mulch type, size, and density. The water flow through the voids for each organic cover, which represents the inverse of the ∆ value, can be crucial for the surface runoff to begin, impacting water and soil losses. In Figure 9 the Brachiaria grass had the largest drained depth, with a value of approximately 1.2 higher than cashew cover.

It is noteworthy that a statistical difference was observed based on the size of the organic covers, with higher water retention associated with smaller sizes. After rainfall events, the water trapped in the substrates will be partially evaporated and partially transmitted to the soil.

To investigate the combined effects of the factors “size”, “density”, and “type” on absorption and retention depths, a Principal Component analysis was carried out, as presented in Figure 10. The analysis reveals that mulching density, specifically at 8 t ha⁻¹, is the primary variable influencing the variability of water retention and absorption.

Component 1 accounts for more than 94% of the variability in the retention and absorption depths. Mulch densities of 8 t ha⁻¹, regardless of type or length, exhibit behavior that is distinct to the other variables. Additionally, grasses contribute similarly to the variability, with a recommended size of 50 mm; the variable “size” is identified as the second most important factor.

Moreover, the “type” variable contributes the least to the variability of absorption and retention depths. However, Brachiaria grass and cashew leaves are the main types accounting for the total variance in water absorption and retention. Based on Figure 10a,b, it can be verified that density is the primary variable influencing water absorption.

Consequently, a lumped experimental value based on the mean measurements for all mulch types together is presented in Figure 11, covering both the rainfall period and the drainage period (after rainfall cessation). Exponential fitting is also presented for each mulch size and density, which are the two most important factors.

It was observed that canopy saturation was achieved during the rainfall period (lasting 10 min and delivering a gross amount of 15 mm), and that drainage from the mulch surface reached an equilibrium. Note that the ∆ value was inverted, in order to represent drainage, reflecting the potential increase in soil moisture beneath the mulch.

The optimal parameters for the exponential model and the fitting metric values are presented in Table 5. The coefficient of determination, Willmott, and performance coefficients were all very high. Hence, the adopted exponential model with three parameters represented the experimental data well.

As $c_1$ is associated with a threshold interception depth, either in terms of canopy storage capacity or of drainage from canopy surface, it is worthwhile analyzing c₁ as function of mulch size and mulch density.

It can be verified that a linear multiple regression model fits the experimental data well (from Table 5) as follows: for the rainfall period, R² = 0.9692 (Equation (5)) and for the no-rainfall period, R² = 0.9692 (Equation (6)).

(5)$c_1=0.4970+0.1588 MD-0.0009 MS$

(6)$c_1=0.3257+0.0306 MD-0.0006 MS$

where $MD$ is the mulch density and $MS$ is the mulch size, referring to the mean behavior of both interception (storage capacity) and drainage potential of the mulching materials (jointly) adopted in this study.

In this study, a contrasting behavior has been found for 200, 100, and 50 mm sizes. According to Figure 7, which is consistent with the multiple linear model developed from the lumped analysis, an increase in size produces a decrease in mulch storage, according to the mean experimental data for coconut tree, elephant grass, Brachiaria, sugar cane, cashew leaves, and corn leaves jointly.

4. Discussion

4.1. General Behavior of Retained and Absorbed Depths

The highest densities showed the largest depths being retained and absorbed. It was observed that coconut leaf (cc), cashew leaf (ca), and elephant grass (el) mulch covers presented the largest retention depths. Other mulch covers could be eventually studied but these six apparently have more potential, particularly presenting a high carbon versus nitrogen ratio [28].

Although a rainfall duration of 10 min is common, it was chosen to be smaller than other reported studies on surface and sub-surface processes (Confessor and Rodrigues [34], which used a period of 30 min).

4.2. Initial Absorption by Different Mulch Covers

The results obtained for the initial angle of the water absorption curve by the mulches quantify the observations of several studies that report that the varied densities have an immediate effect on the soil erosion control, reducing soil erodibility. In this study, adopting an intense rainfall, the importance of using vegetation cover and higher densities is highlighted by the greater initial retention; that is, there is a reduction in the kinetic energy on the soil as observed by Dunkerley [1] and Montenegro et al. [6], and for the first minute after the event beginning, low rainwater depth is transmitted to the soil surface.

Yang et al. [43] observed that the soil coverage by corn straw in a no-tillage system allowed greater grain yield and higher water use efficiency due to the lower runoff and the soil moisture conservation effect.

The results for greater initial retention associated with higher densities can be explained by the greater amounts of surface materials available for adhesion and cohesion processes, in addition to the absorbed and retained water on the mulch micro relief. Niziolomski et al. [36] also associates the greater retention capacity with the increase in the surface roughness, enhancing friction to the flow, and increased storage associated with micro-obstacles in the vegetation cover. As mulch density increases, it is expected that friction will be higher, as explained by the results presented in Figure 5.

4.3. Absorption and Retention by Different Covers

Dosages greater than 8 t ha⁻¹ of elephant grass have already been used for the purpose of controlling agricultural and hydrological pathogens, showing similar results due to the amount of soil-covered area. Yagi et al. [44] observed positive results for the use of chopped elephant grass at a dose of 60 t ha⁻¹ (84.8% humidity), which corresponds to approximately 9 t ha⁻¹ in dry mass, which was applied over an area cultivated with potato.

Qu et al. [45] used different soil mulches including organic ones which, despite having varying sizes, did not display changes in soil moisture content, and were still superior to the inorganic mulches, successfully decreasing the soil evaporation rate.

Considering all mulch types and sizes, the highest absorptions and retentions are observed for 8 t ha⁻¹ density. Niziolomski et al. [36] observed that in addition to water retention at the surface of high-density mulch treatments (already reported above), the time for the beginning of runoff is controlled by the formation of barriers over the cover/soil interface, particularly when density is high, enhancing water storage that delays runoff generation and promotes higher infiltration rates.

Considering all mulch types and sizes, the highest absorptions and retentions are observed for 8 t ha⁻¹ density. Niziolomski et al. [36] observed that in addition to water retention by the surface of high-density mulch treatments (already reported above), the time for the beginning of runoff is controlled by to the formation of barriers over the cover/soil interface, particularly when density is high, enhancing water storage that delays runoff generation and promotes higher infiltration rates.

4.4. Depth Drained by Different Covers

The use of organic cover dosages of around to 2 t ha⁻¹ already promotes the retention/absorption of a significant amount of rainwater when compared to residual values (Figure 9 Bottom), delaying runoff and consequently water and soil losses. It was also verified by Cerdà et al. [18], in a study with a 30-year database, observing that the use of soil cover with oat straw at 1.25 t ha⁻¹ provided a runoff of 7 to 1.9% of precipitation, with an average value of 4.2%. And for the control (bare soil), the discharge was greater, varying from 12.7 to 3.3% of the rainfall, with an average of 7.3%.

Storage capacities vary depending on the geometric shape of the cover, with the highest storage capacity observed for coverage with Brachiaria grass. When this capacity is exceeded, water can be transmitted efficiently to the soil due to less kinetic energy. Similarly, the use of natural cover allows for retention that could have resulted in greater runoff if interception had not occurred, thereby increasing water use efficiency, especially for light rainfalls [1].

Lima et al. [26] used rice straw (Oryza sativa L. ssp. japonica) with three sizes: 10 mm, 30 mm, and 200 mm and found that, under rainfall of 84 mm h⁻¹ intensity and a duration of 15 min, the rice straw capacity to absorb water is greater for the longer stripes (~70% by weight) and lower for the 10 mm and 30 mm sizes (respectively with 25% and 55%). Furthermore, the absorption capacity of the rice straw was estimated by placing 100 g of dry mulch on a 20% slope covered with an impermeable sheet.

5. Conclusions

This paper evaluates the retention and absorption of mulch cover under simulated rainfall in semiarid Brazil. Different organic covers with varying sizes and densities were studied.

As expected, increasing the density of mulching resulted in higher water retention. It was observed that increasing the application density systematically led to an increase in water retention and absorption.

An application density of 8 t ha⁻¹ improved rainfall retention and absorption by over 100% compared to 2 t ha⁻¹, retaining 7–23% of rainfall depth under the studied conditions.

Cashew leaf and Brachiaria grass outperformed coconut leaves, which retained only 83% and absorbed 67% of the water compared to them.

Smaller organic mulch sizes enhanced water retention, with retained depths ranging from 7 to 23% and 5–18% and absorbed depths from 4 to 18% and 2–14%, as rainfall depth increased from 50 mm to 200 mm.

Using organic cover dosages of 2 t ha⁻¹ significantly contributed to retention and absorption, delaying runoff onset, increasing water infiltration, and reducing water and soil losses. Mulching covers with an application density of 8 t ha⁻¹ showed the highest water retention and absorption for all types and sizes. Future work should focus on developing comprehensive frameworks, including specific thresholds or guidelines for practical applications, to enhance the effectiveness and reliability of the proposed methodologies.

Author Contributions

Conceptualization, I.L. and A.A.A.M.; methodology, I.L., A.A.d.C. and A.A.A.M.; software, I.L.; validation, I.L., A.A.A.M., J.L.M.P.d.L. and A.A.d.C.; formal analysis, I.L., A.A.A.M. and J.L.M.P.d.L.; investigation, I.L., A.A.A.M., J.L.M.P.d.L. and A.A.d.C.; data curation, I.L. and A.A.A.M.; writing—original draft preparation, I.L., A.A.A.M. and A.A.d.C.; writing—review and editing, I.L., A.A.A.M., J.L.M.P.d.L. and A.A.d.C.; visualization, I.L., A.A.A.M., J.L.M.P.d.L. and A.A.d.C.; supervision, A.A.A.M.; project administration, A.A.A.M.; funding acquisition, I.L. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Acknowledgments

We thank the Postgraduate Program in Agricultural Engineering (PGEA) of the Federal Rural University of Pernambuco (UFRPE) for supporting the development of this research.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures and Tables

Figure 1. Photographs of coconut leaf (cc), cashew leaf (ca), elephant grass (el), corn leaf (co), Brachiaria grass (br), and sugar cane leaf (su).

Figure 2. (a) Sketch of the laboratory setup: 1—constant head reservoir; 2—valves; 3—pump; 4—manometer; 5—oscillating nozzle; 6—weighing device; 7—mulch support; and 8—support structure of the rainfall simulator. (b) View of the measuring device.

Figure 3. Sketch with variables involved in water retention and absorption processes in time for a given organic mulching cover for a rainfall with 10 min duration. α—angle referring to initial retention intensity and ∆—drained seepage depth. Tp is rainfall duration and Td is drainage time after rainfall. A is the maximum water retention value and B is the stabilized value of retained water.

Figure 4. Water retention and absorption by the different mulch covers for different mulch sizes and densities: coconut leaf (CC), cashew leaf (CA), elephant grass (EL), corn leaf (CO), Brachiaria grass (BR), and sugar cane leaf (SU) (see Figure 1). In the graphs, vertical scales change with mulch density.

Figure 5. Initial water retention angle α (°) for 10 min after rainfall start, and 5 min after rainfall stop as a function of mulch type (a), size (b), and density (c) for coconut leaf (CC), cashew leaf (CA), elephant grass (EL), corn leaf (CO), brachiaria grass (BR), and sugar cane leaf (SU). The letters above the columns represent the statistical result of the Tukey test.

Figure 6. Water retention (after 10 min) and absorption (after 15 min). Depth in mm (on top) and as a percentage of rainfall (on bottom) for all mulch types (all mulch sizes and all densities). In the figure, the red asterisks correspond to outlier values. The letters above the columns represent the statistical result of the Tukey test.

Figure 7. Depth retained (after 10 min) and absorbed (after 15 min), in mm (top) and as percentage of rainfall (bottom), for different mulch sizes (all mulch types and all densities). In the figure, the red asterisks correspond to outlier values. The significant regression coefficients are for p [less than] 0.05 (*) (black).

Figure 8. Depth retained (after 10 min) and absorbed (after 15 min) in mm (top) and as percentage of rainfall (bottom) for mulch densities (all mulch type and all sizes).

Figure 9. Difference in water retention between 10 min and 15 min depths (5 min after rainfall ends) as a function of mulch type, size, and density. In the figure, the red asterisks correspond to outlier values. The letters above the columns represent the statistical result of the Tukey test. The significant regression coefficients are for p [less than] 0.05 (*) (black).

Figure 10. Principal Component Analysis for absorption and retention depths (a) considering separately the variables densities (1, 2, 4, 8 t ha−1 mulch) (b), sizes (200, 100, 50 mm) (c), types (coconut leaf (CC), cashew leaf (CA), elephant grass (EL), corn leaf (CO), Brachiaria grass (BR), and sugar cane leaf (SU)) (d), and type with their sizes (e), distributed by clusters.

Figure 11. Top: rainfall interception depth for 1, 2, 4, and 8 t ha−1 mulch densities and for 50, 100, and 200 mm mulching sizes ((a), (b), (c), respectively). Bottom: drainage for 1, 2, 4, and 8 t ha−1 mulch densities and 200, 100, and 50 mm mulching sizes ((d), (e), (f), respectively).

Table 1

Criteria for interpreting the performance index “c”.

Index “c”	Classification
˃0.85	Great
0.76–0.85	Very good
0.66–0.75	Good
0.61–0. 65	Median
0.51–0.60	Sufferable
0.41–0.50	Poor
≤0.40	Very poor

Table 2

Analysis of variance for size, type, and density for initial depth retention angle α (°).

SV	DF	SS	MS	CfV	F
Type	5	1258.2	251.6	14.5	0.00
Size	2	314.9	157.4	9.1	0.00
Density	3	11,286.6	3762.2	217.3	0.00
$\mathrm{Type} \times \mathrm{Size}$	10	526.0	52.6	3.0	0.00
Type × Density	15	428.5	28.5	1.6	0.07
Size × Density	6	83.8	13.9	0.8	0.57
Type × Size × Density	30	648.4	21.6	1.2	0.20
error	150	2595.8	17.3
Total	215	17,058.8

SV—Source of Variation; DF—Degree of Freedom; SS—Sum of Squares; MS—Mean Square; CfV—Critical F Value; F—F-test.

Table 3

Analysis of variance for water retention as a function of factors type, size, and density.

SV	DF	SS	MS	CfV	F
Type	5	4.4	0.8	23.2	0.00
Size	2	0.9	0.4	12.0	0.00
Density	3	40.9	13.6	357.8	0.00
Type × Size	10	1.8	0.1	4.8	0.00
Type × Density	15	1.0	0.07	1.8	0.02
Size × Density	6	0.4	0.07	1.9	0.07
Type × Size × Density	30	1.0	0.03	0.8	0.65
Error	144	5.4	0.03
Total	215	56.1

SV—Source of Variation; DF—Degree of Freedom; SS—Sum of Squares; MS—Mean Square; CfV—Critical F Value; F—F-test.

Table 4

Analysis of variance for drained seepage depth, as a function of factors type, size, and density.

SV	DF	SS	MS	CfV	F
Type	5	0.5	0.1	14.9	0.00
Size	2	0.2	0.1	14.6	0.00
Density	3	1.0	0.3	46.1	0.00
Type × Size	10	0.3	0.0	4.9	0.00
Type × Density	15	0.1	0.0	1.0	0.42
Size × Density	6	0.1	0.0	2.3	0.03
Type × Size × Density	30	0.3	0.0	1.3	0.13
error	144	1.1	0.0
Total	215	3.8

SV—Source of Variation; DF—Degree of Freedom; SS—Sum of Squares; MS—Mean Square; CfV—Critical F Value; F—F-test.

Table 5

Exponential model parameters and performance for different mulching densities and sizes, regardless mulching types. The first value corresponds to density and the second to size (x;xx).

Rainfall Interception
	1; 200	2; 200	4; 200	8; 200	1; 100	2; 100	4; 100	8; 100	1; 50	2; 50	4; 50	8; 50
c1	0.41	0.64	1.05	1.60	0.45	0.85	1.05	1.55	0.55	0.83	1.14	1.75
a	1.90	2.20	2.30	2.70	1.90	2.20	2.30	2.60	2.00	2.20	2.50	2.80
alfa	0.50	0.70	0.80	0.86	0.50	0.70	0.80	0.86	0.50	0.70	0.80	0.90
R2	0.97	0.97	0.99	0.99	0.97	0.98	0.99	0.99	0.97	0.99	0.98	0.99
d	0.99	0.99	1.00	1.00	0.96	0.99	1.00	1.00	0.99	0.99	0.99	1.00
c	0.98	0.98	0.99	0.99	0.95	0.98	0.99	1.00	0.97	0.99	0.98	0.99
Rainfall Drainage
	1; 200	2; 200	4; 200	8; 200	1; 100	2; 100	4; 100	8; 100	1; 50	2; 50	4; 50	8; 50
c1	0.23	0.27	0.37	0.45	0.26	0.36	0.43	0.43	0.30	0.34	0.46	0.58
a	0.70	0.80	0.90	1.00	1.00	1.00	1.20	1.40	1.00	1.00	1.20	1.50
alfa	0.50	0.50	0.50	0.50	0.50	0.50	0.50	0.50	0.40	0.70	0.50	0.60
R2	0.98	0.99	0.99	0.99	0.99	0.99	1.00	0.99	0.99	0.99	0.99	1.00
d	0.99	0.99	1.00	1.00	0.96	0.99	1.00	1.00	0.99	0.99	0.99	1.00
c	0.98	0.98	0.99	0.99	0.95	0.98	0.99	1.00	0.97	0.99	0.98	0.99

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