Influence of Vegetation Cover and Soil Properties on Water Infiltration: A Study in High-Andean Ecosystems of Peru

Abstract

Water infiltration into soil is a key process in regulating the hydrological cycle and sustaining ecosystem services in high-Andean environments. However, limited information is available regarding its dynamics in these ecosystems. This study evaluated the influence of three types of vegetation cover and soil properties on water infiltration in a high-Andean environment. A double-ring infiltrometer, the Water Drop Penetration Time (WDPT, s) method, and laboratory physicochemical characterization were employed. Soils under forest cover exhibited significantly higher quasi-steady infiltration rates (i_s, 0.248 ± 0.028 cm·min⁻¹) compared to grazing areas (0.051 ± 0.016 cm·min⁻¹) and agricultural lands (0.032 ± 0.013 cm·min⁻¹). Soil organic matter content was positively correlated with i_s. The modified Kostiakov infiltration model provided the best overall fit, while the Horton model better described infiltration rates approaching i_s. Sand and clay fractions, along with K⁺, Ca²⁺, and Mg²⁺, were particularly significant during the soil’s wet stages. In drier stages, increased Na⁺ concentrations and decreased silt content were associated with higher water repellency. Based on WDPT, agricultural soils exhibited persistent hydrophilic behavior even after drying (median [IQR] from 0.61 [0.38] s to 1.24 [0.46] s), whereas forest (from 2.84 [3.73] s to 3.53 [24.17] s) and grazing soils (from 4.37 [1.95] s to 19.83 [109.33] s) transitioned to weakly or moderately hydrophobic patterns. These findings demonstrate that native Andean forest soils exhibit a higher infiltration capacity than soils under anthropogenic management (agriculture and grazing), highlighting the need to conserve and restore native vegetation cover to strengthen water resilience and mitigate the impacts of land-use change.

1. Introduction

Soil is a fundamental natural resource for the sustainability of terrestrial ecosystems and human well-being [1]. It provides physical support for plant, animal, and microbial life while regulating critical processes such as the hydrological cycle, carbon storage, and nutrient supply [2]. Furthermore, soil underpins global food security, with approximately 95% of food production relying directly or indirectly on this resource [3]. Therefore, soil conservation is essential to ensure the provision of key ecosystem services, including biodiversity preservation, water regulation, and climate change mitigation [4].

Soil is essential for human life, providing food, space, and critical environmental services. It is a social asset that must be valued and protected. In high-Andean regions, its significance is heightened by its role in sustaining fragile ecosystems and supporting communities living under extreme conditions [5]. High-Andean soils possess unique characteristics, including a high organic matter content and a porous structure that enhances water and nutrient retention, facilitating carbon storage within the soil [6]. These properties are crucial for ensuring water regulation, carbon sequestration, and maintaining agricultural productivity in these agroecosystems [7]. However, the ecological fragility of high-Andean soils makes them highly susceptible to degradation, which compromises their capacity to provide essential ecosystem services and undermines the sustainability and well-being of the communities that depend on them [8].

In this context, studying infiltration processes requires a thorough understanding of soil characteristics and associated factors. Vegetation type, in particular, plays a significant role in influencing the positive or negative impacts on mountain ecosystems [9]. For instance, in the high mountains of Asia, the presence of trees and shrubs has been shown to have more favorable effects compared to alpine meadow vegetation [9]. Similarly, in the Ecuadorian páramos, native vegetation contributes significantly to hydrological regulation processes, namely, infiltration, water mobilization, and retention [10]. In European alpine environments, research conducted in Iceland has demonstrated how vegetation cover and freeze–thaw cycles influence infiltration in Andisols, revealing comparable mechanisms across mountain ecosystems at different latitudes [11].

Land degradation is a global issue that has a particularly severe impact on high-Andean regions [12], significantly impairing soil functionality and the provision of ecosystem services [7]. This degradation is driven by both natural factors, such as steep slopes and intense rainfall, as well as anthropogenic activities, including deforestation, overgrazing, and the intensive use of unsustainable agricultural practices [13]. Among these factors, land-use change toward agricultural activities is one of the most critical [14]. The conversion of native forests and grasslands into agricultural or urban land directly alters landscape structure, affects biodiversity, reduces vegetation cover, and disrupts natural soil cycles, such as infiltration, water retention, and carbon storage [15,16]. Recent studies suggest that such transformations can result in a 16% reduction in soil organic carbon (SOC) stocks, thereby contributing to climate change, decreasing ecosystem resilience, and limiting soil fertility for crop production [8,16,17]. Moreover, in high-Andean soils, the presence of steep slopes and landscape fragmentation exacerbates these impacts, further increasing their vulnerability to climate change [16,17].

On the other hand, soil compaction resulting from the use of heavy machinery and intensive grazing adversely affects soil structure by increasing bulk density, reducing macroporosity and infiltration capacity, and thereby impairing nutrient and water retention. These changes ultimately lead to a decrease in agricultural productivity [14,18]. Furthermore, the loss of organic matter, driven by practices such as monoculture and lack of crop rotation, reduces soil biodiversity, which in turn negatively impacts soil fertility [14].

One of the most significant consequences of soil degradation is the reduction in soil infiltration capacity, which is a process critical to the hydrological cycle [19]. Infiltration enables water to penetrate the soil, be stored within its pores, and recharge aquifers [20]. When soil is degraded, infiltration decreases, leading to increased surface runoff, heightened erosion, and a higher risk of flooding [14,21]. Seasonal rainfall further exacerbates soil erosion, resulting in the loss of fine particles and essential nutrients, reduced water availability, and a decline in overall soil quality [19,22].

In response to these challenges, the appropriate management of soils based on their suitability is essential for ensuring their conservation and enhancing their capacity to provide ecosystem services. Sustainable land management practices, such as implementing living barriers, crop rotation, and ecological restoration, have proven effective in improving soil quality, increasing organic matter content, and restoring ecological functions [23]. These practices not only enhance infiltration and reduce compaction but also contribute to carbon sequestration and improved soil biodiversity [24].

Assessing the impacts of productive activities on high-Andean soils is essential for developing sustainable strategies that conserve soil and biodiversity while ensuring both environmental and social resilience [25]. However, due to the inherent complexity of high-Andean systems, significant knowledge gaps remain regarding the dynamics of runoff and infiltration associated with the intricacies of human–environment systems [26]. In this context, analyzing infiltration capacity across different productive systems in mountainous watersheds provides valuable baseline information on how management practices influence soil properties. This approach would enable the identification and proposal of strategies to mitigate soil degradation and ensure ecosystem resilience in the face of climate change impacts. Given the pronounced spatial heterogeneity of the Andes and the limited number of studies in areas facing critical water challenges, such as the Peruvian Andes [27], this research aims to evaluate and compare soil infiltration capacities across agricultural, forestry, and grazing productive systems in a high-Andean region of northern Peru. This study seeks to elucidate the impact of productive activities on soil quality and to propose sustainable management practices that foster soil conservation and the preservation of ecosystem services in this high-Andean region.

2. Materials and Methods

2.1. Study Area

The study area is located within the Chotano River basin, in the province of Chota, Cajamarca region, Peru (6°31′52.55″ S; 78°38′50.75″ W). The specific locations of the sampling sites are detailed in

Table 1. Location of sampling sites and land cover characteristics in the Chotano River sub-basin, Cajamarca.

Land Cover	Symbol	Length	Latitude	Altitude (masl)	Predominant Species
Agricultural	A	6°29′37.91″	78°37′14.89″	3010	Area dominated by the cultivation of potatoes (Solanum tuberosum), ollucos (Ullucus tuberosus), corn (Zea mays) and vegetables [31,32].
		6°29′54.01″	78°37′20.90″	2980
		6°30′3.62″	78°37′8.10″	3985
Forestry	F	6°32′15.35″	78°41′23.57″	2246	Area dominated by endemic species (Hedyosmum scabrum, Palicourea amethystina, Weinmannia elliptica, Lauraceae, Myrtaceae, and Melastomataceae, Brachyotum coronatum, Cyathea caracasana, Axinaea nitida, and Ocotea jumbillensis) [33,34].
		6°32′22.72″	78°41′55.81″	2240
		6°32′11.35″	78°42′0.19″	2245
Grazing	G	6°35′4.34″	78°39′19.56″	2380	Area dominated by established crops of alfalfa, ryegrass, and clover [31,32].
		6°35′1.15″	78°39′33.47″	2395
		6°35′29.97″	78°39′31.41″	2390

, and their spatial distribution is illustrated in Figure 1. The basin exhibits a temperate humid climate typical of the Andean region with an average annual temperature of 15.6 °C and minimal seasonal variation. The average annual precipitation is 958.1 mm, following a bimodal distribution with peaks between February and April and September to November, and minimum rainfall occurring from June to August [28]. The predominant slopes range from 15% to 75% with altitudes varying between 2400 and 3500 masl. At the geomorphological level, steep mountains, hills, and high plateaus are identified, which are composed of lithological formations of Cretaceous sedimentary rocks, volcanic rocks, and metamorphic rocks. In terms of soil classification, andosols (shallow to moderately deep soils with high moisture retention capacity and medium fertility, suitable for livestock and forestry activities), cambisols (moderately deep soils appropriate for improved pasture cultivation), and leptosols (very shallow, low-fertility soils mainly designated for protection lands) are identified in the area. The region is also subject to intensive agricultural activities that exacerbate these geomorphological processes [29]. According to land-use capacity classifications, the area includes land suitable for clean cultivation, permanent cultivation, forestry production, pasture, and protection lands [30].

2.2. Field Methods

Using a square shovel, soil samples were collected from each study site at a depth of 0.30 m. Sampling was conducted on 25 January 2025, which was four days after the last recorded precipitation in the study area (Figure S1). Prior to storage and transport to the laboratory, gravel and large plant debris were removed from the samples. The samples were then sent to the Soil, Water, and Foliar Laboratory (LABSAF) at the Baños del Inca—Cajamarca Agrarian Experimental Station of the National Institute of Agrarian Innovation (INIA). Each sample was subjected to physicochemical characterization, including measurements of pH [35], electrical conductivity (EC) [36], texture [37], organic carbon content, and organic matter content using the Walkley and Black method (AS-07) [37], and interchangeable bases (Ca²⁺, Mg²⁺, Na⁺, K⁺) [37]. Soil moisture content values at Field Capacity (FC; matric potential: −0.33 MPa) and Permanent Wilting Point (PWP; matric potential: −1.5 MPa) were estimated according to the method described by Saxton and Rawls [38].

In addition, field infiltration trials were conducted using the double-ring infiltrometer method [39]. An initial water load of 0.21 m was applied and replenished after every 0.08 m of water level drop. Instantaneous infiltration rates (cm·min⁻¹) were calculated based on the relationship between the infiltrated depth and the infiltration time. Periodic measurements of both parameters were recorded, and the test was concluded when the instantaneous infiltration rate remained constant over three consecutive intervals.

Water repellency was assessed using the Water Drop Penetration Time (WDPT) test. Representative, homogeneous, and undisturbed soil samples were collected from each evaluated area (i.e., within 2 m² around each study point) at a depth of 0.15 m. Soil clods were carefully cleaned to remove plant debris, stones, and other materials that could interfere with the measurements. Samples were air-dried for two weeks, both outdoors and indoors, before testing. The WDPT tests were conducted ex situ under controlled conditions (18 °C and 60% relative humidity). Five drops of distilled water (0.05 mL) were applied to the lateral surface of each sample using a single-channel adjustable-volume micropipette (Glassco Laboratory Equipments Pvt. Ltd., Haryana, India), taking care not to disturb the soil structure [40]. A stopwatch was used to record the time required for the complete absorption of each drop. The procedure was repeated five times for each sample. The WDPT values were classified according to Bisdom et al. [41] as follows: hydrophilic (<5 s), weakly hydrophobic (5–60 s), moderately hydrophobic (60–600 s), strongly hydrophobic (600–3600 s), and extremely hydrophobic (>3600 s).

2.3. Hydraulic Models

2.3.1. Modified Kostiakov Model

This model is commonly applied to agricultural and grazing soils that experience surface compaction from trampling, due to its simplicity and its ability to adjust to field conditions where infiltration typically occurs rapidly at first, then decreases sharply, and eventually stabilizes or becomes limited [42]. The method describes infiltration as a function of time, $\left({\mathrm{f}}_{\left(\mathrm{t}\right)}\right)$ [43], as follows:

$${\mathrm{f}}_{\left(\mathrm{t}\right)}=\mathrm{k}·{\mathrm{t}}^{\mathrm{a}}+{\mathrm{f}}_{\mathrm{c}}$$

(1)

where ${\mathrm{f}}_{\mathrm{c}}$ is the quasi-steady infiltration rate, $\mathrm{t}$ is time, and $\mathrm{k}$ and $\mathrm{a}$ are empirical coefficients.

2.3.2. Philip Model

The model integrates two components: soil sorptivity, which dominates during the initial stages of infiltration, and gravitational flow, which becomes increasingly significant over time [44]. It is particularly useful for undisturbed forest soils, where natural structural conditions, such as the presence of organic matter, active roots, and macropores, facilitate sustained and uniform infiltration [45]. The infiltration rate as a function of time, ${\mathrm{f}}_{\left(\mathrm{t}\right)}$, is expressed as follows:

$${\mathrm{f}}_{\left(\mathrm{t}\right)}={\displaystyle \frac{1}{2}}\mathrm{S}·{\mathrm{t}}^{-1⁄2}+\mathrm{A}$$

(2)

where $\mathrm{S}$ is the soil sorptivity or capillary conductivity, $\mathrm{A}$ is the gravitational flow coefficient, and $\mathrm{t}$ is time.

2.3.3. Horton Model

This empirical model describes the exponential decrease in infiltration rate from an initially high value to a constant or final rate [46]. It is widely used to simulate infiltration events under rainfall or intensive irrigation conditions. It is particularly suitable for recently tilled agricultural soils and grazing lands, where surface compaction drastically affects infiltration over time. In forest environments, the Horton model can also be applied to represent the progressive reduction in infiltration in soils with surface organic cover or in those where partial pore collapse occurs following prolonged rainfall [47]. For ${\mathrm{f}}_{\left(\mathrm{t}\right)}$, we have

$${\mathrm{f}}_{\left(\mathrm{t}\right)}={\mathrm{f}}_{\mathrm{c}}+\left({\mathrm{f}}_0-{\mathrm{f}}_{\mathrm{c}}\right)·{\mathrm{e}}^{-\mathrm{k}·\mathrm{t}}$$

(3)

where ${\mathrm{f}}_0$ is the initial infiltration rate, ${\mathrm{f}}_{\mathrm{c}}$ is the quasi-steady infiltration rate, $\mathrm{k}$ is the decay constant, and $\mathrm{t}$ is time.

2.4. Statistical Analysis and Model Fitting

Differences between treatments were evaluated using a one-way analysis of variance (ANOVA, α = 0.05) for the physicochemical and hydraulic characterization parameters of the soils. The model’s assumptions of normality and homoscedasticity were verified using the Shapiro–Wilk test and both Bartlett’s and Levene’s tests, respectively. Analyses were performed using the stat package in R 4.3.0 [48]. Post hoc comparisons were conducted using the Least Significant Difference (LSD) test (α = 0.05) with the agricolae package [49]. When the assumptions of normality and homoscedasticity were not met, non-parametric Kruskal–Wallis and Dunn’s tests were applied [50]. Correlations between variables were assessed using Spearman’s correlation coefficient with the stat [48] and the corrplot packages in R [51].

The infiltration models were calibrated using the GRG Nonlinear engine in Excel’s Solver tool. The fitting parameters for each model were defined as variables, and the Coefficient of Determination (R²) closest to 1 was established as the objective function [52]:

$${\mathrm{R}}^2=1-{\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{y}}_{\mathrm{i}}-{\widehat{\mathrm{y}}}_{\mathrm{i}}\right)}^2}{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{y}}_{\mathrm{i}}-\overline{\mathrm{y}}\right)}^2}}$$

(4)

where n is the number of evaluations, ${\mathrm{y}}_{\mathrm{i}}$ is the measured value for evaluation i, ${\widehat{\mathrm{y}}}_{\mathrm{i}}$ is the calculated value for evaluation i, and $\overline{\mathrm{y}}$ is the mean of the measured values for the parameter under analysis. In addition, the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) were calculated considering the previous nomenclature:

$$\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}=\sqrt{{\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{y}}_{\mathrm{i}}-{\widehat{\mathrm{y}}}_{\mathrm{i}}\right)}^2}{\mathrm{n}}}}$$

(5)

$$\mathrm{M}\mathrm{A}\mathrm{E}={\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}\left|{\mathrm{y}}_{\mathrm{i}}-{\widehat{\mathrm{y}}}_{\mathrm{i}}\right|}{\mathrm{n}}}$$

(6)

3. Results

3.1. Associated Physicochemical Characteristics

Soils under forest and grazing cover exhibited significantly higher organic matter (OM) content compared to those used for agriculture, with increases of 257% and 115%, respectively (

Table 2. Physicochemical characteristics of soils according to vegetation cover.

Land Cover	Sand %	Clay %	Silt %	SOM %	TC g·kg−1	pH	EC mS·m−1	Ca2+ cmol(+) kg−1	Mg2+ cmol(+) kg−1	Na+ cmol(+) kg−1	K+ cmol(+) kg−1
Agricultural	39.2 ± 8.4 ab	34.7 ± 7.1 b	26 ± 2.1	5.03 ± 2.86 b	35.7 ± 17.4 c	6.4 ± 0.6 b	10.7 ± 9	13.9 ± 5.98 b	0.87 ± 0.43	0.09 ± 0.05	0.43 ± 0.23 b
Forestry	47.2 ± 6.3 a	31.4 ± 4.8 b	21.4 ± 2.3	17.96 ± 6.50 a	131.7 ± 21.9 a	7.4 ± 0.2 a	32.5 ± 2.7	56.34 ± 12.12 a	3.11 ± 1.46	0.06 ± 0.01	2.62 ± 0.32 a
Grazing	28.5 ± 4.6 b	46.8 ± 2.3 a	24.6 ± 6.1	10.81 ± 0.79 ab	71.9 ± 2.8 b	7.6 ± 0.5 a	25.4 ± 11.5	48.22 ± 10.33 a	2.14 ± 0.33	0.21 ± 0.12	2.05 ± 0.69 a

Notes: SOM: soil organic matter. TC: total carbon content. EC: electrical conductivity. Different letters (a, b, c) represent statistical significance among groups at p < 0.05 for LSD test.

). This trend was also reflected in the total carbon (TC) content, with forest soils containing 2.68 times more TC than agricultural soils, and grazing soils containing 1.01 times more TC than agricultural cover. Soil pH measurements indicated values near neutrality with a slight tendency toward alkalinity in forest (pH 7.4) and grazing soils (pH 7.6). In contrast, agricultural soils displayed slightly acidic conditions (pH 6.4). In all cases, low electrical conductivity (EC) values were recorded (<100 mS·m⁻¹). Soil texture analysis revealed a predominance of sand and clay across all three land covers with no significant differences in silt content. Soils under agricultural cover exhibited a more uniform composition with textures similar to those of clay loam soils. Forest soils showed a significantly higher sand content, reaching 47.2% of their composition, corresponding to a sandy clay loam texture. In contrast, grazing soils were predominantly composed of clay. With regard to exchangeable bases, forest and pasture soils exhibited higher average values than those recorded for agricultural soils. However, statistically significant differences were found only for calcium Ca²⁺ and K⁺. Calcium stood out as the most abundant exchangeable base, while Na showed the lowest concentrations, not exceeding 0.5 cmol(+) kg⁻¹.

3.2. Hydraulic Parameters

The quasi-steady infiltration rates (i_s) showed significant differences between treatments. The highest i_s values were recorded in soils under forest cover (0.248 ± 0.028 cm·min⁻¹). Agricultural and grazing lands exhibited similar values (0.051 ± 0.016 cm·min⁻¹ and 0.032 ± 0.013 cm·min⁻¹, respectively) as shown in Figure 2A. Additionally, soils under grazing cover exhibited higher values of Field Capacity (FC) and Permanent Wilting Point (PWP) (40.8 ± 0.4% and 29.0 ± 1.2%, respectively) compared to agricultural (36.3 ± 2.2% and 22.9 ± 2.5%) and forest soils (36.2 ± 2.0% and 22.7 ± 1.9%) (Figure 2B). However, differences in Total Available Water (TAW) were not statistically conclusive (p = 0.048).

The models for estimating infiltration ratios (i) had coefficients of determination (R²) ranging from 64.5% to 88.0% (Figure 3). On average, the highest R² values were obtained with the Modified Kostiakov model, which was followed by the Horton and Philip models. The models provided a better fit for infiltration processes in agricultural and grazing land covers compared to forested areas. The lowest model fit corresponded to the lowest infiltration rates, approaching the quasi-steady infiltration, where a tendency to overestimate values was observed. The model parameters are detailed in

Table 3. Model parameters evaluated by vegetation cover.

Model	Parameter	Agricultural	Forestry	Grazing
Modified Kostiakov	k	1.639 ± 0.117	2.93 ± 0.418	1.558 ± 0.076
	a	−0.568 ± 0.062	−0.791 ± 0.129	−0.559 ± 0.052
	fc	0.051 ± 0.016	0.096 ± 0.064	0.032 ± 0.013
Philip	S	2.231 ± 0.266	2.813 ± 0.095	2.288 ± 0.09
	A	0.08 ± 0.014	0.038 ± 0.025	0.042 ± 0.018
Horton	f0	0.736 ± 0.053	0.932 ± 0.037	0.73 ± 0.054
	fc	0.051 ± 0.016	0.059 ± 0.064	0.032 ± 0.013
	k	0.028 ± 0.003	0.039 ± 0.008	0.032 ± 0.007

.

The gravimetric moisture contents in the field were 19.3 ± 3.1%, 32.0 ± 4.6%, and 35.7 ± 6.4% for agricultural, forest, and grazing soils, respectively. Significant differences for Water Drop Penetration Time (WDPT) were identified between agricultural soils and those under forest and grazing covers (Figure 4). It was observed that the WDPT under field conditions (WDPT_f) tended to increase as soil moisture decreased. After oven drying, WDPT values (WDPT_d) in forest and grazing soils were associated with repellent soils of weak and medium hydrophobicity. In contrast, agricultural soils remained within the range classified as wettable soils.

Based on the calculated Spearman correlation coefficients (r), significant correlations were identified between quasi-steady infiltration (i_s) and both textural variables (i.e., sand (Sa), clay (Cl)) and carbon-content-related variables (i.e., total carbon (TC), organic carbon (OC), and organic matter (OM)) (Figure 5). Notably, i_s showed positive correlations with sand content and carbon-related variables, while negative correlations were observed with clay content. Additionally, significant positive correlations were found between i_s and nitrogen content (N), while there was a negative correlation with sodium (Na⁺). Significant correlations were also observed between i_s and field capacity (FC; r = –0.46) as well As Permanent Wilting Point (PWP; r = –0.49). For Water Drop Penetration Time (WDPT), the natural logarithm of the median values per sample was used. Significant positive correlations were identified between Ln(WDPT_f) and pH > TC > K⁺ > EC > OM ≈ OC > Ca²⁺ > Mg²⁺, along with a negative correlation with silt content. Regarding Ln(WDPT_d), significant positive correlations were observed with pH > EC > K⁺ > Na⁺ > Mg²⁺, while a negative correlation was found with silt content.

4. Discussion

Runoff generation is directly influenced by soil infiltration processes, which, in turn, are shaped by the soil’s physicochemical properties and the characteristics of the surrounding ecosystem. In this context, the present study identified the key factors affecting infiltration dynamics in soils under extensive agricultural use, forest cover dominated by wild species such as white cedar or tarque (Hedyosmum scabrum), Brasil or silvacho (Palicourea amethystina), and Creole laurel (Ocotea jumbillensis), as well as in grazing areas in the high-Andean region (the main species of each vegetation cover are described in Text S1). Soils under forest cover exhibited significantly higher levels of organic matter (17.96 ± 6.5 g·kg⁻¹) and total carbon (131.7 ± 21.9 g·kg⁻¹) compared to those under agricultural and grazing use. This greater accumulation of organic compounds, combined with a sandy loam texture (47.2 ± 6.3% sand), resulted in quasi-steady infiltration rates (i_s) of approximately 0.25 cm·min⁻¹ in forest soils, exceeding the rates observed in agricultural and grazing soils. These findings align with those reported by Zhang et al. [53], who documented a 30–60% increase in infiltration capacity in forest plantations, which was attributed to their high organic matter content and abundance of active fine roots. Additionally, the prevalence of the sandy fraction likely promoted the formation of macropores, as a lower content of fine particles can reduce soil compaction and enhance water movement through the soil profile [54]. It is also important to emphasize the structural role of organic matter, as root activity in soils under vegetative cover promotes an increase in the proportion of functional pores (>300 µm), thereby facilitating preferential water flow [55].

The lower organic matter (OM) content and reduced infiltration rates observed in agricultural and grazing soils (i_s = 0.05 and 0.03 cm·min⁻¹, respectively) are consistent with previous studies, which report that areas with tree cover typically exhibit greater infiltration capacity compared to grasslands or bare soils [56]. In this regard, soils subjected to greater anthropogenic disturbance often experience a reduction in functional porosity as a consequence of mechanization and trampling, which negatively alter their water dynamics [57]. In our study, the sampled agricultural lands have historically been used under a regime of family farming and local production, which requires the use of tillage machinery. The grazing lands, at the regional level, support an estimated average animal load of 2 AU ha⁻¹ [58]. Ladeira et al. [59] evaluated intensively grazed grasslands and found that animal trampling induces significant surface compaction, reducing macroporosity and decreasing infiltration rates by up to 60% compared to uncompacted soils. In contrast, studies on conservation tillage processes have shown that lower levels of soil disturbance resulting from agricultural activities are associated with higher infiltration rates and the conservation of available moisture [60].

From a physicochemical perspective, a strong positive relationship was identified between the presence of carbon in the soil and i_s, whereas weaker correlations were found with textural fractions. This finding is consistent with that of Fukumasu et al. [61], who reported that the presence of micro- and macropores in agricultural soils is more closely linked to soil organic carbon (SOC) than to clay content, thereby significantly influencing soil–water interactions under unsaturated conditions. In our study, we observed a direct relationship between i_s and sand content, reaffirming the established association between coarse particle size distribution and hydraulic efficiency [62]. Conversely, an inverse relationship was identified between i_s and fine particle content (i.e., clay), which is typically associated with soils prone to compaction following prolonged use [63].

In an agroecological context, infiltration plays a crucial role in key processes, including aquifer recharge, reduction in surface runoff, prevention of water erosion, and regulation of soil biota dynamics. In this regard, soil properties, such as texture, structure, porosity, and organic matter content, as well as the associated vegetation, strongly influence soil–water interactions [64,65]. Additionally, studies have reported that the abundance, richness, and diversity of soil species are positively correlated with the soil’s infiltration capacity [66]. From a microbiological perspective, it has been demonstrated that the depletion of soil microbiota can significantly reduce soil water retention capacity [67,68]. Rhizobiota, through their interactions with plant roots, promote the formation of biopores, which act as preferential pathways for water flow [69]. Moreover, various plant growth-promoting rhizobacteria produce exopolysaccharides that function as adhesive matrices, facilitating the formation of stable soil aggregates [70]. The hyphae of arbuscular mycorrhizal fungi (AMF) further contribute to soil aggregation and enhance pore connectivity, thereby promoting water movement. In addition, microbial activity involved in transforming organic residues into more stable forms can contribute to mineral dissolution and the reduction in soil bulk density, facilitating water infiltration, reducing compaction, and improving soil aeration [71,72]. The relationship between agricultural, forest, and grazing land covers and the microbial composition of the soil is strongly shaped by the level of soil disturbance, vegetation type, organic matter input, and soil management practices associated with each cover type [73,74,75].

In this context, the effectiveness of infiltration models in representing and explaining rainfall–runoff processes depends on soil characteristics and the surrounding environment, underscoring the importance of evaluating their performance under different conditions [76,77]. It is important to note that the evaluated infiltration models (i.e., modified Kostiakov, Horton, and Philip) assume a vertically homogeneous soil profile. This assumption may limit their ability to accurately represent real-world infiltration dynamics in ecosystems characterized by pronounced vertical heterogeneity, such as high-Andean soils, which often contain less permeable layers, compacted horizons, or surface organic accumulations [78]. In such contexts, deeper soil horizons can significantly influence and even control surface infiltration–runoff processes [79]. In the present study, the infiltration models tested tended to yield higher R² values in agricultural and grazing ecosystems. In contrast, they exhibited lower fit in less disturbed systems, such as forested areas. This limitation may be associated with the models’ inability to capture vertical variability in forest soil structure, particularly, the presence of a high-porous, high-hydraulic conductivity organic (O) horizon overlying more persistent impermeable layers [80]. Overall, the Kostiakov model provided the best statistical fit across the three evaluated land covers, with average R²: 0.85, RMSE: 0.098, and MAE: 0.079; this was followed by the Horton (R²: 0.76, RMSE: 0.129, MAE: 0.095) and Philip (R²: 0.73, RMSE: 0.136, MAE: 0.102) models. These results are consistent with those reported by Mesele et al. [62], who compared infiltration models across different soil textures and confirmed the high precision of the modified Kostiakov model (R²: 0.76–0.99) with low errors, emphasizing its adaptability to variable field conditions. Similarly, Al-Janabi et al. [81] demonstrated the flexibility and efficiency of this model in complex environments. In other studies, the Kostiakov model has been reported as the second-best performer after Philip’s model [76], although some research has found lower efficiencies when compared to the Horton and Philip models [82]. In our study, the superior fit of the Kostiakov model appears to be linked to its better representation of initial infiltration rates. However, this advantage diminishes when evaluating values approaching the quasi-steady infiltration rate (i_s), where the Horton model provided a better fit. Both parameters are essential for analyzing infiltration dynamics. The i_s determines the threshold beyond which surface runoff is likely to occur once the soil reaches saturation, while the initial infiltration rate influences the amount of water rapidly infiltrated at the onset of a precipitation event. This highlights the importance of understanding the study’s objective in order to select the appropriate analysis model.

The general trend observed for soils under forest and grazing cover was a transition from mild hydrophobicity under field conditions to moderate hydrophobicity after oven drying, whereas agricultural soils consistently maintained their hydrophilic behavior, even after drying. The logarithm behavior of WDPT_f was positively correlated with total carbon content (r = 0.60) and organic carbon (r = 0.50), supporting the notion that soil water repellency (SWR) is a dynamic parameter closely linked to soil carbon content [83]. Similarly, significant correlations have been reported between WDPT and total organic carbon content (r = 0.706, p < 0.05) in European mountain ecosystems, where hydrophobic compounds derived from plant decomposition (i.e., waxes and lipids) tend to accumulate on the surface of soil aggregates in forest soils with high carbon levels (>50 g·kg⁻¹) [84]. In line with these findings, Roper et al. [85] reported that up to 63% of the variability in WDPT can be explained by organic matter content and the proportion of fine particles. Furthermore, Flores-Mangual et al. [86] demonstrated that plant–soil interactions can determine differences in the vertical distribution of infiltration and that different sources of organic carbon may differentially influence SWR. This highlights the need for further research to disentangle the effects of different ecological associations on infiltration dynamics. Further research is also needed to examine the effects of soil compaction associated with agricultural activities (i.e., hardpans induced by agricultural machinery) and livestock grazing due to animal trampling [24,78,87].

Previous studies have reported a positive relationship between soil carbon and SWR, which is particularly relevant in forestry systems with exotic plantations [88] and in conservation agriculture systems [89]. Additionally, significant positive correlations were observed between the logarithm of WDPT (Ln(WDPT)) and both pH and electrical conductivity (EC), which persisted from field-moist conditions through to oven-dried samples. It has been noted that the presence of K⁺, Ca²⁺, and Mg²⁺ cations exhibits a significant positive correlation when the soil is in field conditions. However, after drying, Na⁺ emerges as a cation of interest, also showing a significant positive correlation. These findings are consistent with Danielsen et al. [90], who reported that K⁺, Ca²⁺, and Mg²⁺ stabilize soil aggregates and enhance the accumulation of hydrophobic compounds on their surfaces, thereby increasing the intensity of soil water repellency (SWR). After drying, Na⁺ was found to promote colloidal dispersion, creating localized zones of water repellency. Moreover, Razipoor et al. [91] emphasized the critical role of interactions between soil carbon content and cations, particularly Ca²⁺ and Mg²⁺, in aggregate formation and soil water retention dynamics. They observed that soils with higher cationic stabilization exhibited lower hydrophobicity after drying, while excess Na⁺ increased soil water repellency. Therefore, our findings confirm that forest cover and higher organic matter accumulation contribute to increased soil hydrophobicity, particularly following periods of drought. This condition can create an initial barrier to infiltration, potentially leading to increased surface runoff and heightened erosion risk during intense rainfall events. Such factors should be carefully considered when designing soil conservation and water management strategies in forested and grazing ecosystems. However, the degree of correlation between the parameters may vary depending on local conditions and land management practices [83]. Therefore, further research is necessary to deepen the understanding of infiltration dynamics across different land covers associated with mountain ecosystems.

Anthropogenic activities can significantly impact soil–water–plant factors by altering their characteristics and directly or indirectly affecting soil–water interactions. Practices such as zero tillage, crop rotation, cover cropping, and integrated systems (i.e., silvopastoral and agroforestry) have been shown to improve infiltration, increase organic matter content, promote aggregate stability, reduce compaction, lower soil erodibility, and prevent soil degradation in agricultural areas [24,54,92]. Soil degradation has profound effects on its functionality and the provision of associated ecosystem services. In particular, a reduced infiltration capacity directly impacts the hydrological cycle, soil biodiversity, and decreases soil fertility as well as the stability and resilience of ecosystems [14,19]. Land-use changes, such as the conversion of native grasslands to croplands or the replacement of shrubland and forest covers with agricultural fields or plantations, can significantly alter soil carbon dynamics with particular concern in high-Andean regions where vulnerability to climate change is heightened [16,17,65]. Additionally, the literature has reported that changes in soil hydrology can influence carbon fluxes, including the release of CO₂ into the atmosphere [93]. Therefore, taken together, our findings, along with previous studies, underscore the importance of considering soil water infiltration as a key indicator of soil health and environmental sustainability rather than merely a physical property.

Over the past 40 years, the Peruvian Andean biome has shown a consistent trend of increasing agricultural and livestock land use (≈+12.1 thousand ha·year⁻¹) alongside a decline in forested areas (≈−7.7 thousand ha·year⁻¹) [94]. However, these land-use change processes vary across regions with observable shifts toward agricultural expansion and the establishment of forest plantations [95,96]. In the Cajamarca region, the natural forest areas have been reduced by the effects of agricultural expansion [31]. In that sense, understanding these change processes and their potential effects on the hydrological cycle could allow the better designing of land-use plans. For example, the estimated infiltration for forested areas does not exceed the estimated intensity for a 1 h design rainfall with a long return period (RP) [97]. In contrast, agricultural and grazing areas would only allow the infiltration of precipitation intensities with RP of 2 to 10 years, respectively. This behavior will depend on the previous state of the soil, since less frequent rainfall could be found in weakly hydrophobic soils. In this sense, the adequate management of conserved and restored areas of native forests could positively affect the soil’s capacity to provide essential ecosystem services such as water regulation and erosion control, mitigate the effects of climate change, and ensure water and food security in rural and high-Andean areas. However, the successful adoption of these technical management approaches largely depends on producers’ willingness to embrace sustainable practices, as the consequences of soil degradation often become evident only in the medium to long term or following high-intensity rainfall events [98].

This study identifies potential limitations that hinder a comprehensive understanding of the processes underlying infiltration dynamics. Due to financial constraints, both the number of variables and the analyzed samples were limited. It is acknowledged that expanding the sampling areas and incorporating a wider range of land management practices within the same land cover category could have enhance the understanding of the spatial variability within each land use category. However, the study aimed to maximize representativeness by selecting sampling sites that reflect typical conditions of agricultural, grazing, and forested lands in the region, as defined by previous land use and soil classification studies. Although not exhaustive, the dataset offers valuable first-order insights into how land use influences infiltration dynamics in high-Andean ecosystems. Future research should incorporate denser sampling grids and seasonal replicates to further explore intra-category heterogeneity and strengthen generalizability of findings. In addition, greater attention should be given to vertical interactions between soil horizons, soil structure, and their relationship with topographic factors such as slope. Furthermore, as previously discussed, the chemical characteristics of the organic matter source (i.e., carbon-to-nitrogen ratio) influence its interaction with the soil; therefore, analyzing these properties would have enhanced our understanding of their relationship with infiltration processes. Despite these limitations, this study provides broader insights into the understanding of infiltration dynamics and the calibration of hydraulic models under mountainous watershed conditions in the high-Andean region.

5. Conclusions

This study provides valuable insights into the factors influencing infiltration processes in high-Andean areas. During wet soil stages, sand and clay fractions and K⁺, Ca²⁺, and Mg²⁺ play a key role, while the silt fraction and Na⁺ become more relevant under dry soil conditions. Thus, forestry soils exhibited the highest quasi-steady infiltration rate (i_s: 0.248 ± 0.028 cm·min⁻¹), surpassing those of grazing and agricultural (0.051 ± 0.016 cm·min⁻¹ and 0.032 ± 0.013 cm·min⁻¹, respectively). In addition, agricultural soils consistently displayed hydrophilic behavior throughout the drying process, in contrast to forest and grazing soils, which transitioned toward weakly to moderately hydrophobic patterns as drying progressed. This shift, associated with higher carbon content and cationic interactions, may pose a risk for delayed infiltration and increased surface runoff following dry periods, highlighting the importance of considering soil water repellency in water and erosion management. In this context, the modified Kostiakov model provided a better fit. However, it tended to overestimate values near the i_s region, where Horton’s model demonstrated a superior fit.

The findings underscore the critical role of land management in shaping infiltration processes and suggest that conserving or restoring native vegetation cover is essential to enhancing soil–water interactions, regulating runoff, and strengthening ecosystem resilience in the Andes. At the public policy level, our findings can serve as a basis for designing and updating land use plans in Andean regions. Thus, it is estimated that in the face of less frequent rainfall events, the capacity to rapidly absorb precipitation persists in agricultural soils compared to grazing and even forested areas. However, in the face of frequent, long-duration events, forest cover soils would withstand greater volumes of infiltrated water before generating surface runoff.

Based on that, it is important to complement this type of research with hydrological modeling that allows for the evaluation of potential effects of different climate change and land-use scenarios. Additionally, future research should aim to incorporate a broader spatial and temporal dataset, including additional landscape contexts, varied altitudinal gradients, and multiple seasons, to expand our understanding of infiltration dynamics. Moreover, deeper investigation into the vertical distribution of infiltration, the role of microbial communities, and the chemical nature of soil organic matter would enhance the interpretation of hydrological behavior under different land uses. Overall, this study establishes a foundational framework for evaluating infiltration as a key indicator of soil health and water regulation in high-Andean regions with practical implications for sustainable land management and climate change adaptation.