Optimization of Irrigation Efficiency and Water Retention in Agroecological Systems Through Organic Matter Management

Abstract

Water scarcity poses a critical constraint to sustainable agriculture, particularly in small-scale systems that rely on traditional irrigation methods. Although organic matter (OM) is known to enhance soil structure and water-holding capacity, quantitative evidence regarding optimal OM levels and their interaction with microbial activity in agroecological contexts remains limited. This study evaluates the effect of different OM contents (2.37%, 3.42%, 5.55%, 7.89%, and 9.43%) on infiltration, moisture retention, and microbiological dynamics in 129 agroecological plots located in the northern highlands of Ecuador. Field and laboratory assessments revealed that intermediate OM levels (between 3.42% and 5.55%) optimize available water retention (up to 14.78%) and stabilize infiltration. In contrast, excessive OM levels (>7.9%) decrease retention efficiency and increase leaching risk. Microbial activity showed a positive correlation with OM up to a certain threshold, beyond which fungal and yeast activity declined under field conditions. The results underscore the importance of managing OM within an optimal functional range to improve irrigation efficiency, enhance microbial resilience, and support water sustainability in agroecological production systems.

1. Introduction

Sustainability in agriculture faces critical challenges, among which efficient water management and soil conservation stand out [1]. Globally, agriculture is estimated to consume approximately 70% of available freshwater resources [2,3], exerting considerable pressure on ecosystems and productive systems. Simultaneously, soil degradation caused by organic matter loss, compaction, and erosion directly affects its water-holding capacity, thereby reducing agricultural productivity and increasing reliance on intensive irrigation practices [4,5].

In the Andean region of Ecuador, where smallholder farming systems predominate, these conditions are exacerbated by traditional soil and water management practices, as well as by limited irrigation technification, with efficiency rarely exceeding 58% [6]. Despite the recognized importance of organic matter in improving soil structure and water management, there is limited quantitative evidence that integrally links organic matter content, soil microbial activity, and irrigation efficiency in agroecological systems.

In recent years, agroecological systems have gained relevance as alternatives to conventional agricultural models, prioritizing soil health, biodiversity, and the sustainable use of natural resources such as water [7]. International evidence shows that these systems, when combined with traditional knowledge and practices, can significantly improve soil health through the incorporation of organic matter [8]. In Ecuador, agroecological initiatives—mostly led by women farmers—have emerged as a counterpoint to the agroextractive floriculture model, whose cultivated area grew by 42% between 2016 and 2023 [9]. However, key challenges persist: irrigation management remains largely empirical, with limited consideration of soil physical and microbiological properties or its actual water retention capacity.

Irrigation efficiency depends on multiple factors, including climatic conditions, technologies employed, and soil management practices [10,11]. Among these, organic matter plays a particularly relevant role due to its multiple functions: improving soil structure, enhancing infiltration, increasing water retention, and stimulating microbial activity [12,13,14]. These functions are especially critical in Andean soils, where degradation, compaction, and low fertility constrain the efficient use of available water [15]. Although several studies have demonstrated the contribution of OM to the physical and chemical improvement of soils [16], few have simultaneously integrated microbial dynamics and water management under agroecological principles.

Moreover, the effects of organic matter on soil microbiota are neither linear nor universal. Several studies indicate that high concentrations of organic matter can create anoxic conditions or alter the carbon-to-nitrogen ratio, thereby affecting the functional microbial diversity and its metabolic efficiency [17,18].

Recent studies on the application of varying levels of beetle-derived organic matter have shown improvements in soil physical properties, including particle reorganization, reduced bulk density, and increased volumetric moisture content [19]. Moreover, the role of specific microorganisms in OM decomposition is well-documented: bacteria such as Bacillus subtilis and Pseudomonas fluorescens efficiently degrade simple compounds like cellulose [20,21], while fungi like Trichoderma harzianum and Aspergillus niger can decompose more complex materials such as lignin [22,23]. Actinobacteria such as Streptomyces spp. also play a key role in breaking down recalcitrant plant residues [17,24].

Despite these findings, it remains unclear whether there is a functional threshold of organic matter that simultaneously optimizes water use efficiency and microbial activity. This study aims to address that gap by experimentally evaluating the behavior of these variables under real field conditions and controlled laboratory settings.

Organic matter decomposition not only improves nutrient availability, but also increases porosity and the soil’s water-holding capacity, allowing for a greater proportion of applied water to remain available to plants for longer periods [25]. These processes collectively enhance water use efficiency in agricultural systems, which is also influenced by human practices.

In this context, the present study aims to evaluate the effect of different levels of organic matter on soil microbial stability, water retention capacity, and irrigation efficiency, to identify an optimal functional range that supports both hydrological and biological sustainability in agroecological smallholder farming systems in the Ecuadorian Andes.

2. Materials and Methods

2.1. Study Area

The study was conducted in 129 agroecological plots distributed across various territories within the Cayambe canton, located in Pichincha Province in the northern highlands of Ecuador (latitudes 0°1′ N to 0°10′ N and longitudes 78°5′ W to 78°15′ W). This area lies on the eastern slope of the Andean mountain range and features a variable topography, with elevations ranging from 2700 to 3200 m above sea level (Figure 1). The climate is classified as humid mesothermal Andean, influenced by the Intertropical Convergence Zone. Fieldwork was carried out from February 2024 to May 2025.

Average annual precipitation ranges from 750 to 1200 mm, concentrated between October and May, while mean annual temperature fluctuates between 12 °C and 16 °C, with high microclimatic variability associated with altitudinal zones.

From a pedological perspective, the predominant soils are Andisols, with localized occurrences of Inceptisols and Entisols on hillslopes. Dominant soil textures vary from sandy loam in the lower areas to silty loam and clay loam at higher elevations, which significantly influence both water retention capacity and the dynamics of organic matter [26]. In some areas, soils exhibit naturally high organic matter content (OM > 5%), resulting from the accumulation of plant residues, low temperatures, and minimal anthropogenic disturbance.

2.2. Methodological Approach

A mixed-method approach was employed, integrating quantitative (experimental) and qualitative (descriptive) techniques to establish cause–and–effect relationships between the independent variables (OM content and presence of microorganisms) and dependent variables (soil water retention capacity and irrigation efficiency). The study was carried out in two complementary phases: a field stage and a laboratory experimental stage, each with its own procedures, techniques, and instruments (Figure 2).

2.3. Field Stage

Out of the total 129 agroecological plots, 15 were selected for detailed analysis of soil microbial activity and irrigation efficiency. This selection was carried out using stratified sampling based on organic matter (OM) content, defined according to the quartiles of its statistical distribution. Five levels were established: Level I (OM 2.37%), Level II (3.42%), Level III (5.55%), Level IV (7.89%), and Level V (OM > 9.43%), with three plots selected per level.

The selection criteria included spatial and altitudinal representativeness (2700–3200 m a.s.l.) as well as agricultural diversity, featuring horticultural species such as lettuce, carrot, onion, cabbage, beet, pea, potato, bean, and broccoli. This strategy ensured comparability among groups and captured the environmental heterogeneity of the Cayambe region.

2.3.1. Infiltration Rate

The basic infiltration rate was measured using a double-ring infiltrometer installed at approximately 10 cm depth, ensuring proper leveling of the rings, following standard protocols [27,28]. The depth of infiltrated water was recorded at regular intervals until a constant rate was reached, which was considered the basic infiltration rate. When no stabilization occurred, surface runoff was checked as an indication of saturation or structural limitations. Infiltration was estimated using the empirical Kostiakov equation (Equation (1)):

$$I=a{t}^{b }$$

(1)

where I is the infiltration rate (mm h⁻¹ or cm h⁻¹), t is the contact time between water and soil (minutes or hours), a is the initial infiltration coefficient, and b is a dimensionless exponent ranging from 0 to −1 depending on soil type.

2.3.2. Soil Moisture

Soil moisture was determined using the standard gravimetric method [29]. Undisturbed samples were collected at three profile depths: 0–10 cm, 10–20 cm, and 20–30 cm, using 100 cm³ metal cylinders. Three replicates were taken at each level of organic matter (OM). Samples were weighed in their wet state (fresh weight) and then oven-dried at 105 ± 2 °C for 24 h, following the guidelines of Klute [30]. Gravimetric moisture content (θg) was calculated using Equation (2).

$$\mathsf{\theta }\mathrm{g}\left(\mathrm{\%}\right)={\displaystyle \frac{\mathrm{W}\mathrm{s}-\mathrm{D}\mathrm{s}}{\mathrm{D}\mathrm{s}}}\times 100$$

(2)

where Ws is the wet sample weight (g) and Ds is the dry sample weight (g). This value represents the amount of water retained in the soil at the time of sampling and provides an assessment of the soil moisture status in the profile [31].

2.3.3. Soil Moisture Retention Capacity

Field capacity (FC) and permanent wilting point (PWP) were determined, as these parameters define the range of water available to plants [32]. Undisturbed samples were collected at three depths: 0–10 cm, 10–20 cm, and 20–30 cm, representing the effective root exploration zone in annual crops. The samples were saturated with distilled water and subjected to controlled matric potentials using a Richards pressure plate extractor. They were equilibrated at −0.033 MPa for FC and −1.5 MPa for PWP. Subsequently, the samples were oven-dried at 105 ± 2 °C to calculate gravimetric moisture content.

Available water (AW) was estimated as the difference between FC and PWP at each depth. To obtain an integrated estimate of the available water capacity (AWC) for the profile, a weighted average of the values obtained from the three layers was calculated, taking into account their relative thickness. This procedure provides a more accurate approximation of the water effectively available to plant roots, in line with Moratelli [33]. Available water (AW) was calculated using the following equation (Equation (3)):

$$AWCtotal=D1i=1\sum n\left[\left(\theta FCi-\theta PWPi\right)\times di\right]$$

(3)

where ${\theta }_{F{C}_i}$: soil moisture at field capacity for layer $i$, ${\theta }_{PW{P}_i}$: soil moisture at permanent wilting point for layer $i$, $d_i$: thickness of soil layer $i$(cm), $D=\sum d_i$: total depth of the root zone evaluated, $n$: number of soil layers (in this study, $n=3$), ${\mathit{AWC}}_{\mathit{total}}$: total available water capacity integrated over the profile.

2.3.4. Evaluation of Irrigation Efficiency

In the agroecological plots of the northern highlands of Ecuador, irrigation is carried out through community-based gravity-fed systems, requiring no pumping, by utilizing water sources located at higher elevations. Pressure at the hydrants ranged between 30 and 60 PSI, with drops of up to 5 PSI at the nozzles; in some cases, pressures exceeding 120 PSI were reported.

The plots, ranging from 200 to 1000 m², are typically irrigated using a single sprinkler approximately 1 m in height. Identified models included the Senninger 5023 (5 and 2 mm nozzles, 706 m² coverage) and the Xcel-Wobbler (3 mm nozzle, 38 m² coverage), although many devices lacked technical specifications.

Irrigation efficiency was assessed based on three variables: application rate, uniformity coefficient, and application efficiency. To this end, rainfall distribution tests were conducted using 100 rain gauges arranged in a 10 × 10 grid within the sprinkler coverage area [11]. The spacing between rain gauges was adjusted according to the effective reach of each system. Each trial involved a continuous water application for 60 min, with the volume collected in each rain gauge recorded. During measurements, wind speed was low (0.7 to 1.4 m/s), which minimized drift and allowed for uniform water distribution. The application rate (Ar) was estimated using Equation (4).

$$\mathrm{A}\mathrm{r}={\displaystyle \frac{\mathrm{V}\times {10}^3}{\mathrm{A}\times \mathrm{t}}}$$

(4)

where Ar is the application rate (mm h⁻¹), V is the volume collected in the rain gauge (m³), A is the rain gauge area (m²), and t is the duration of the test (h).

The uniformity coefficient was calculated using Christiansen’s formula (Equation (5)):

$$CU\left(\%\right)=100 \left[1-{\displaystyle \frac{\begin{array}{c}{\rm N} \hfill \\ \sum \left|Z_{\mathrm{i}}-Z_{\mathrm{a}}\right|\\ i=1 \end{array}}{{\rm N}{Z}_{\mathrm{a}}}} \right]$$

(5)

where CU is the uniformity coefficient, Z_i is the depth of water collected in each rain gauge, Z_a is the mean depth of water collected across all rain gauges, and N is the total number of rain gauges used in the evaluation.

To characterize the crops present in the agroecological plots at the time of the field evaluation, they were grouped according to their life cycle duration in days: 60–80 days (chamomile, mint, oregano), 80–100 days (pea, fava bean, cabbage), 100–120 days (common bean, rosemary, chili), 120–140 days (broccoli, lemon verbena, bell pepper), 140–160 days (maize, lemon balm, leek), more than 200 days (peach, strawberry, garlic), and more than 300 days (tree tomato, mountain papaya). For each group, an average crop coefficient (Kc) value was estimated, taking into account their respective phenological stages: establishment, development, maturity, and harvest.

Reference evapotranspiration (ETo) was calculated using the CROPWAT 8.0 software, based on the FAO Penman-Monteith model, which incorporates local climatic data including maximum and minimum temperature (°C), relative humidity (%), wind speed (m/s), and sunshine duration (hours/day). This information allowed for adjustment of the crop evapotranspiration (ETc) calculation according to the type and phenological stage of the crops present in the plots during the irrigation trials.

Application efficiency (Ae) was calculated according to Playán [9], as the ratio of crop water requirements to the actual volume applied (Equation (6)):

$$Ae\left(\%\right)={\displaystyle \frac{Wr}{RDW}}\times 100$$

(6)

where Ae represents water use efficiency at the plot level, expressed as a percentage; Wr corresponds to the crop water requirements, estimated using the CROPWAT software; and RDW is the actual amount of water applied (m³ ha⁻¹).

2.3.5. Soil and Microbiological Analysis

Soil organic matter (OM) content was determined using the Walkley–Black method [34,35], which is based on the oxidation of organic carbon with 1 N potassium dichromate in an acidic medium, followed by titration with 0.5 N FeSO₄ using ferroin as an indicator. Before this analysis, soil samples were dried and sieved through a 2 mm mesh.

The organic carbon values were corrected using a factor of 1.33 and converted to total organic matter, with results expressed as a percentage (% OM) on a dry weight basis [35].

Microbial activity was assessed by quantifying three groups: mesophilic aerobes, fungi, and yeasts. Aerobes were cultured using Petrifilm plates (AOAC 990.12) [36]. Fungi and yeasts were inoculated on Bengal Rose agar with chloramphenicol (Sigma-Aldrich) at 10⁻¹ to 10⁻³ dilutions and incubated at 25 ± 2 °C. Colony counts were performed after 5 days for fungi and yeasts, and 24–48 h for aerobes (ISO 21527-2) [37].

Microbial abundance was assessed using the 10⁻³ dilution data, as it provided the most representative counts. All analyses were performed in triplicate during both field and laboratory phases.

2.3.6. Statistical Analysis of Field Data

The data obtained were processed using descriptive statistics to characterize the evaluated variables. Subsequently, a one-way analysis of variance (ANOVA) was applied to identify significant differences between treatments. When statistical differences were detected, Tukey’s multiple comparison test was used, with a 95% confidence level (p < 0.05). All analyses were performed using InfoStat software, version 2020.

2.4. Laboratory Experimental Stage

At this stage, 15 experimental units were implemented under a completely randomized design (CRD). Five treatments with different percentages of organic matter (OM) were evaluated: T1 = 1.56%, T2 = 2.89%, T3 = 4.77%, T4 = 7.69%, and T5 = 9.20%, with three replicates per treatment. The data obtained were processed using descriptive statistics and a one-way analysis of variance (ANOVA), followed by Tukey’s test (p < 0.05) for mean comparisons. All analyses were performed using InfoStat software, version 2020.

Infiltration Trial

Lysimeters with a capacity of 5.28 L were used as experimental units. Each was filled with 6.90 kg of a homogeneous mixture of soil and humus. The OM-to-soil ratio for each treatment was adjusted using the dilution formula (Equation (7)), in kilograms:

$$\mathrm{C}1\cdot \mathrm{V}1=\mathrm{C}2\cdot \mathrm{V}2$$

(7)

where C₁ and V₁ represent the concentration and volume (or mass) of the initial material with known OM content, and C₂ and V₂ represent the desired concentration and volume in the final mixture.

Tensiometers were installed in five lysimeters, one per treatment, randomly located, allowing for continuous monitoring of soil moisture.

Each lysimeter was initially saturated with 425 mL of water, applied gradually. Then, 210 mL per day was applied over four days. Infiltration was calculated as the difference between applied and drained water volume [38].

3. Results

3.1. Organic Matter and Its Relationship with Infiltration and Soil Moisture

The fitted curves for different levels of soil organic matter (OM) exhibited determination coefficients (R²) ranging from 0.8566 to 0.9607, indicating a good fit to the potential model for most treatments evaluated (Figure 3).

Soils with 2.37% and 7.89% OM exhibited stabilization in infiltration rates, as indicated by their infiltration curves and respective exponents (b = −0.527 and −0.219). Although not the most negative, these values suggest a relatively faster approach to steady state, likely due to favorable pore continuity and aggregate structure enhancing early-stage water infiltration.

In contrast, treatments with 3.42%, 5.55%, and 9.43% OM did not exhibit a defined stabilization within the evaluation period. Notably, the soil with 9.43% OM showed the weakest model fit (R² = 0.8566) and a less negative exponent (b = −0.25), potentially due to greater structural heterogeneity or surface compaction that disrupts pore continuity despite the high organic content.

These findings confirm that the effect of OM on infiltration is neither linear nor uniform. While OM incorporation generally improves porosity and structural stability, its impact depends on factors such as soil texture, bulk density, and irrigation management practices [39].

Regarding the basic infiltration rate (BIR), estimated from the steady phase of the fitted model, a significant variation was observed among treatments. The plot with 2.37% OM showed a BIR of 22.6 cm/h, while the plot with 5.55% registered the lowest rate (12.1 cm/h), and the 7.89% OM plot exhibited the highest rate (69.7 cm/h). The plot with 9.43% OM had a BIR of 49.7 cm/h, and the one with 3.42% reached 17.6 cm/h.

Analysis of variance (ANOVA) revealed statistically significant differences among treatments (F = 5.62; p = 0.038), confirming that OM influences infiltration, though not in a linear manner. According to Tukey’s test (α = 0.05), the BIR in the 7.89% OM treatment was significantly higher than in the 3.42% and 5.55% OM treatments. These results support the hypothesis of an optimal functional range of OM that maximizes infiltration without inducing excessive percolation.

3.2. Soil Moisture Content and Water Retention Capacity

The physical attributes of the soil evaluated in the agroecological plots showed moderate variability associated with organic matter (OM) content and local management practices. Bulk density ranged from 0.95 to 1.20 g·cm⁻³, with lower values observed in soils with higher OM content. Total porosity, calculated based on a particle density of 2.65 g·cm⁻³, ranged from 54% to 64%, indicating good soil structure in most sampling units.

Regarding water retention, field capacity (FC) moisture ranged from 23.5% to 32.7%, while permanent wilting point (PWP) moisture ranged from 11.8% to 17.4%, depending on soil texture and OM level. These values reflected a variation in soil moisture content from 16.87% in soils with 2.92% OM (Level 1) to 27.39% in soils with 10.36% OM (Level 5). In parallel, retention capacity increased from 10.08% to 14.48% (

Table 1. Soil moisture content and retention capacity as a function of organic matter percentage.

Field Plots						Laboratory Experimental Trial
Level	Organic Matter (%)	Moisture Content (%)		Moisture Retention Capacity (%)		Treatment	Organic Matter (%)	Moisture Content (%)		Moisture Retention Capacity (%)
I	2.37	16.87 ± 0.42	a	10.08 ± 0.14	a	T1	1.56	13.65 ± 0.66	a	8.95 ± 0.30	a
II	3.42	20.85 ± 2.71	ab	11.61 ± 1.03	ab	T2	2.89	17.86 ± 0.64	ab	10.44 ± 0.50	ab
III	5.55	21.82 ± 4.44	ab	12.05 ± 1.86	ab	T3	4.77	22.84 ± 0.93	bc	12.40 ± 0.70	bc
IV	7.89	23.28 ± 4.69	b	12.67 ± 1.93	b	T4	7.69	25.55 ± 0.76	cd	13.58 ± 1.09	cd
V	9.43	27.39 ± 3.24	b	14.48 ± 1.55	b	T5	9.2	28.11 ± 0.92	d	14.78 ± 0.89	d

Note: Different letters within the same column indicate statistically significant differences between treatments, according to Tukey’s test (p < 0.05).

). However, when analyzing retention efficiency, a relative decrease was observed, suggesting a higher proportion of free water, which is more prone to losses through evaporation or percolation [40].

Laboratory trials corroborated the field results. Moisture content increased from 13.65% to 28.11% and water retention from 8.95% to 14.78% across treatments with increasing OM levels (1.56% to 9.20%). However, from approximately 4.77% OM onward (T3), a slight decline in relative retention efficiency was observed, reflecting field trends. Notably, treatments with intermediate OM levels—around 3.42% (T2) and 5.55% (T3)—showed greater consistency between field and laboratory data, suggesting improved structural stability of retained water under varying test conditions.

3.3. Relationship Between Organic Matter and Microorganisms

Both field and laboratory soil analyses revealed significant differences in microbial dynamics depending on OM content (

Table 2. Soil microbial content according to organic matter percentage in field plots and laboratory experimental trial.

Field Plots								Laboratory Experimental Trial
Le-vel	Organic Matter (%)	Log CFU/g Mesophilic		Log CFU/g Fungi		Log CFU/g Yeasts		Treatment	Organic Matter (%) (Post)	Log CFU/g Mesophilic (Post)		Log CFU/g Fungi (Post)		Log CFU/g Yeasts (Post)		EC Start		EC End
I	2.37	9.50 ± 0.05	b	5.30 ± 0.05	b	6.40 ± 0.05	b	T1	1.6	7.80 ± 0.10	c	5.30 ± 0.10	b	6.00 ± 0.10	d	215.1 ± 5.00	a	171.1 ± 6.27	a
II	3.42	9.50 ± 0.05	b	5.50 ± 0.10	c	6.00 ± 0.05	b	T2	2.9	7.60 ± 0.10	b	5.10 ± 0.10	a	5.70 ± 0.10	b	374.4 ± 4.40	b	339.1 ± 4.04	b
III	5.55	9.80 ± 0.05	c	5.80 ± 0.10	d	6.40 ± 0.10	b	T3	4.8	6.80 ± 0.10	a	5.40 ± 0.10	b	5.60 ± 0.10	a	589.1 ± 9.00	c	337.4 ± 7.50	b
IV	7.89	9.30 ± 0.05	a	4.70 ± 0.10	a	4.30 ± 0.10	a	T4	7.7	7.60 ± 0.10	b	5.50 ± 0.10	c	5.90 ± 0.10	c	758.1 ± 8.00	d	389.0 ± 9.00	c
V	9.43	9.30 ± 0.05	a	4.80 ± 0.10	a	5.00 ± 0.10	a	T5	9.2	7.70 ± 0.10	b	5.60 ± 0.10	d	5.60 ± 0.10	a	917.6 ± 7.50	c	433.5 ± 8.50	d

Note: Different letters within the same column indicate statistically significant differences between treatments, according to Tukey’s test (p < 0.05).

). In field samples, microbial activity (particularly fungi and yeasts) declined at OM levels of 7.89% and 9.43%. In contrast, soils with intermediate OM levels (3.42% and 5.55%) exhibited more balanced microbial activity, suggesting the presence of an optimal threshold for microbiota development under natural conditions. This behavior can be explained by the relationship between OM content, oxygen availability, and the carbon-to-nitrogen (C/N) balance—factors that determine microbial respiration rates and the efficiency of organic matter mineralization. When the concentration of organic compounds is excessive, intermediate metabolites (such as volatile organic acids and phenols) can accumulate, exerting toxic or inhibitory effects on sensitive microbial populations [41].

Moreover, soils with high OM tend to retain more moisture and exhibit reduced porosity, creating microaerophilic conditions that limit the growth of aerobic microorganisms and favor the proliferation of fermentative anaerobic groups [42]. This reduction in oxygen availability directly affects the activity of oxidative enzymes and dehydrogenases, which are responsible for breaking down complex compounds such as lignin and cellulose, ultimately lowering the overall microbial respiration rate [43].

Under laboratory conditions, microbial response differed: populations of mesophilic aerobes, fungi, and yeasts remained relatively stable across all treatments, with no significant variations associated with OM content (p > 0.05). The lower dispersion of values among replicates reflects the homogeneity of microbial activity under controlled conditions, where the elimination of environmental fluctuations (rain, wind, temperature) reduces thermal and moisture variability and stabilizes soil metabolic processes [44]. A slight decrease in bacterial counts was observed in T3 (4.8% OM) compared to T1 (1.6%), suggesting a non-linear response.

The physicochemical parameters measured in the laboratory support these observations. pH remained slightly alkaline (7.1–7.3), which favors microbial activity. Electrical conductivity (EC), on the other hand, showed an increasing trend with rising OM levels, reaching initial values of 215 µS/cm in soils with low OM (1.6%) and up to 917 µS/cm in soils with high OM (9.2%). This pattern indicates a greater accumulation of solutes and soluble salts associated with the mineralization of organic compounds, consistent with reports from systems with high loads of available carbon [42]. However, within each treatment, EC decreased by the end of the experiment, suggesting leaching processes induced by continuous irrigation applications [45].

Overall, results indicate that OM modulates microbial activity, but its effects are highly conditioned by edaphoclimatic context [40,41]. In field conditions, structure, aeration, and water dynamics allow clearer observation of OM–microbiota interactions. In the laboratory, environmental stability reduces variability, making the influence of OM less evident. This highlights the importance of considering not only the quantity but also the quality of organic matter and water management practices to support functional microbial communities [18,46].

3.4. Irrigation Efficiency

The results from the sprinkler irrigation systems showed that efficiency is not solely dependent on organic matter (OM) content, but rather on its interaction with crop water demand and management strategies (

Table 3. Application efficiency of sprinkler irrigation in agroecological plots according to soil organic matter content.

Organic Matter (%)	Application Rate (mm/h)	Uniformity Coefficient (%)	Required Water Depth (mm)	Irrigation Frequency (Days)	Irrigation Duration (Hours)	Applied Water Depth (mm)	Application Efficiency (%)
2.37	14.08	69.2	3.33	5	3.00	42.24	39%
3.42	7.89	80.7	2.55	5	2.00	15.78	81%
5.55	10.58	69.5	2.53	4	3.00	31.74	31%
7.89	11.36	74.7	2.66	8	2.00	22.72	84%
9.43	14.29	83	3.11	8	4.00	57.16	41%

).

Plots with intermediate OM levels (3.42% and 7.89%) achieved the highest application efficiencies (81% and 84%, respectively), with applied irrigation depths of 15.78 mm and 22.72 mm. These values aligned well with the calculated crop water requirements (2.55 mm and 2.66 mm per event) and were applied with good uniformity (uniformity coefficients of 80.7% and 74.7%, respectively).

In contrast, soil with either low (2.37%) or high (9.43%) OM content exhibited the lowest application efficiencies (39% and 41%, respectively), despite receiving the highest irrigation depths (42.24 mm and 57.16 mm). In the former case, poor retention was attributed to a sandy loam texture and limited structural stability; in the latter, to excessive development of macropores associated with organic aggregates that promote deep percolation [47,48].

Notably, the 9.43% OM treatment exhibited the highest uniformity coefficient (83%) but achieved only 41% efficiency, demonstrating that good water distribution does not guarantee efficient use if the irrigation system does not account for the soil’s actual retention capacity and the crop’s water demand [49].

These findings confirm that irrigation efficiency results from a complex interaction between soil physical properties—particularly OM content—irrigation scheduling, and alignment with crop water requirements. Therefore, it is essential to design management schemes tailored to local conditions, avoiding both under- and over-irrigation [50,51].

4. Discussion

A significant relationship was observed between soil organic matter (OM) content and infiltration dynamics during infiltration, as well as its influence on water use efficiency in the evaluated agricultural systems. In general, soils with higher OM content exhibited higher infiltration rates than those with lower levels, indicating that OM incorporation improves soil structure, reduces compaction, and increases porosity, thereby facilitating water entry into the soil profile [17].

However, a higher infiltration rate does not necessarily equate to an agronomic advantage. While it may enhance soil aeration and water availability in the root zone, it can also increase the risk of nutrient leaching if irrigation volumes and frequencies are not properly adjusted [40,52]. This is supported by laboratory findings where reduced electrical conductivity was observed in soils with higher infiltration, suggesting potential losses of salts and nutrients through percolation [53].

In this regard, it is necessary to adjust irrigation depth and frequency based on soil OM content and crop water demand to improve efficiency within the root zone and prevent leaching losses. This approach is supported by previous studies recommending water management according to the soil’s retention capacity to optimize irrigation efficiency in agroecological systems [15,54].

In sandy loam soils or those with excessive OM content, water may infiltrate beyond the root zone, thereby reducing irrigation efficiency [54]. This phenomenon is particularly critical in agroecological systems where organic amendments (e.g., compost, green manure) are applied intensively, but without precise water management planning [55]. In the studied plots, most farmers did not employ modern irrigation technologies, and irrigation decisions were based on visual observation of surface soil moisture [6]. While such practices rely on farmers’ experience, they can be inefficient if the actual plant-available water in the soil profile is not considered, leading to over-irrigation and leaching losses [56].

Cisneros et al. [15] emphasize that the lack of technical planning and inadequate knowledge of crop-specific water requirements negatively impact system efficiency and productivity. In this regard, the data from this study reinforce the need to implement irrigation strategies adapted to soil OM content. In soils with high OM—and consequently greater water retention capacity—it is advisable to reduce the irrigation depth and increase irrigation frequency to optimize water availability in the root zone and prevent deep percolation losses [4,15,57].

Complementarily, the results allow for the identification of an optimal functional range of OM—between 3.42% and 7.89%—within which irrigation efficiency is maximized, water retention capacity is improved, and microbial communities are stabilized. Values outside this range tend to generate inefficiencies, either due to limited moisture retention or excessive percolation, consistent with findings from other studies on water saturation in organic soils [42,53].

Despite the robustness of these findings, some limitations must be acknowledged, particularly the heterogeneity of edaphoclimatic and management conditions across plots, such as soil texture, land-use history, and local microclimates, which may have influenced the magnitude of observed responses.

From a biological perspective, soil OM—understood here as the total organic matter content, including both labile and humic fractions—plays a crucial role in the formation of stable aggregates by promoting the activity of soil fauna and filamentous fungi, which contribute to consolidating soil structure [44,51,52]. Humic substances, which can account for up to 60% of total OM, are key to nutrient retention and structural stability. However, their molecular complexity limits their immediate availability to microorganisms, favoring the selection of specialized microbial groups [53,58].

Among these substances, humic acids stand out for their ability to regulate soil pH and electrical conductivity through interactions with available ions, directly influencing nutrient availability and microbial activity [12,59]. These effects were also observed in the field: although higher fungal abundance might be expected in soils with elevated OM, mesophilic bacteria were predominant. This trend could be attributed to environmental factors such as aeration, rainfall, and temperature variations that influence microbial balance [59]. In contrast, under controlled laboratory conditions, the fungi-to-bacteria ratio was more balanced, confirming that soil biological efficiency depends not only on OM content but also on environmental context [18].

Although the microbiological analysis in this study was limited to population counts (mesophilic aerobes, fungi, and yeasts), this approach allowed for a direct comparison of microbial responses to different levels of organic matter (OM) under controlled conditions. However, it is acknowledged that this constitutes a limitation, as it excludes the non-culturable fraction of microorganisms and does not fully reflect the functional diversity of the soil microbiota.

The results emphasize the importance of considering not only the quantity, but also the quality of OM, along with adaptive irrigation practices, to promote functional microbial communities [18].

Consequently, it is recommended to implement an integrated soil management strategy that combines the moderate and gradual incorporation of OM with adaptive irrigation practices, especially in clayey or volcanic Andean soils, where soil structure is particularly sensitive to excess moisture. Additionally, assessing the type of OM—its origin, degree of decomposition, and C/N ratio—allows for the selection of inputs with a greater positive impact on soil biological activity and its capacity to retain water [45,46,59].

In summary, the results suggest that intermediate OM levels (between 3.42% and 5.55%) are optimal for balancing infiltration, moisture retention, and efficient water use. In contrast, extreme values—either too low or too high—tend to generate inefficiencies in water dynamics. Additionally, the interaction between OM and the soil microbiota is highly dependent on environmental conditions, implying that irrigation management in agroecological systems should be adapted not only to soil physical properties but also to its biological dynamics.

This study presents several limitations that should be considered when interpreting the results. First, the heterogeneity in edaphoclimatic and management conditions across plots—including differences in soil texture, land-use history, and local microclimates—may have influenced the variability observed in infiltration rates, water retention, and microbial activity. Although sampling methods and statistical analyses were employed to mitigate these effects, not all potential interactions among these factors were controlled.

Second, the microbiological analysis was limited to basic population counts, which restricts the functional interpretation of soil communities and their relationship with water-use efficiency; future research could incorporate indicators such as microbial biomass, enzymatic activity, or functional diversity.

Finally, while this study establishes an association between organic matter (OM) content and the soil variables related to irrigation efficiency, it is recognized that such efficiency also depends on key agronomic factors, including crop type, phenology, management strategies, and plant–soil interactions. These elements were not addressed in depth in this work but represent essential components for a holistic understanding of water management in agroecological systems.

5. Conclusions

Soil organic matter (OM) content significantly modulates soil water dynamics, influencing infiltration rate, moisture retention capacity, and irrigation efficiency. Based on the findings, an optimal functional OM range between 3.4% and 7.9% was identified, within which available water retention is maximized and leaching losses are minimized.

Extreme OM values, whether low or excessive, cause imbalances that reduce water use efficiency. In high-OM soils, the development of macropores and deep percolation compromises effective water retention, while in low-OM soils, compaction and reduced porosity limit infiltration and the storage of plant-available water.

Regarding soil microbiota, microbial activity is strongly influenced by both the quantity and quality of OM, as well as by soil and climatic conditions. Under field conditions, mesophilic bacteria predominated even in high-OM soils, whereas in laboratory conditions, a more balanced distribution among fungi, bacteria, and yeasts was observed. This confirms that soil biological efficiency results from the interaction of physical, chemical, and environmental factors.

Irrigation efficiency is not only determined by uniform water distribution, but also by the system’s ability to adapt to the soil’s physical and biological characteristics. Adjusting irrigation depth and frequency based on OM content and crop water demand improves water availability in the root zone, reduces leaching risks, and optimizes resource use.

In agroecological contexts with silty loam or clay loam Andean soils, an integrated soil management approach is recommended—one that prioritizes moderate OM incorporation and adaptive irrigation practices. This approach fosters more rational water use, enhances soil biophysical resilience, and strengthens the sustainability of production systems in the face of climate variability.