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Article

Groundcovers Improve Soil Properties in Woody Crops Under Semiarid Climate

by
Blanca Sastre
1,*,
Omar Antón-Iruela
1,2,
Ana Moreno-Delafuente
1,
Mariela J. Navas
3,
Maria Jose Marques
4,
Javier González-Canales
1,5,
Juan Pedro Martín-Sanz
1,
Rubén Ramos
1,
Andrés García-Díaz
1 and
Ramón Bienes
1
1
Madrid Institute for Rural, Agricultural and Food Research and Development (IMIDRA), Finca El Encín, Carretera A-2, km 38.2, Alcalá de Henares, 28805 Madrid, Spain
2
GRUPOPTIMA, 28890 Madrid, Spain
3
Chemical Farmaceutic Depatment, Farmacy Faculty, Universidad Complutense de Madrid, Pza. Ramón y Cajal S/N, 28040 Madrid, Spain
4
Geology and Geochemistry Department, Universidad Autónoma de Madrid, Calle Francisco Tomás y Valiente, 7, 28049 Madrid, Spain
5
Doctorate School, University of Alcalá (UAH), Alcalá de Henares, 28801 Madrid, Spain
*
Author to whom correspondence should be addressed.
Agriculture 2024, 14(12), 2288; https://doi.org/10.3390/agriculture14122288
Submission received: 29 October 2024 / Revised: 10 December 2024 / Accepted: 10 December 2024 / Published: 13 December 2024
(This article belongs to the Special Issue Soil Conservation in Olive Orchard)
Browse Figures
Versions Notes

Abstract

:
There is a worldwide need to enhance soil health, particularly in agricultural areas. Groundcovers are widely recognized sustainable land management (SLM) practices that improve soil health and provide climate benefits by sequestering atmospheric carbon. A paired-plots study was carried out in woody crops (17 sites, olive groves and vineyards) in a semiarid area of central Spain to measure soil parameter changes induced by different management practices in the medium term. The selection across different locations aimed to determine whether the impact of groundcovers was substantial enough to produce significant changes in the studied soil parameters, even when accounting for variations in soil types. Each site consisted of neighboring plots: One was managed with conventional tillage (CT). The other was managed with an alternative soil management practice: (1) spontaneous groundcovers (GC) or (2) no soil management (NM). Vegetation and soil parameters were measured in spring 2021. Despite the low aboveground biomass in GC (77 g m−2), this treatment improved soil organic carbon stock (+4.4 Mg ha−1), infiltration rate (+50%), and aggregate stability (+35%) compared to CT, but higher compaction along the profile was detected. NM only resulted in a better infiltration rate, with high soil compaction. Our study provides supplementary information to long-term studies, which may include soil biological parameters as soil health indicators and yield response. Outcomes of these soil assessments lend support to the implementation of agricultural policies that promote GC as a SLM practice, in order to extend this technique to woody crops.

1. Introduction

Land degradation is a pervasive, systemic phenomenon occurring all over the world [1]. According to the FAO [2], soil degradation is defined as a change in soil health status resulting in a diminished capacity of the ecosystem to provide goods and services for its beneficiaries. The processes of soil degradation can be classified as physical, chemical, and biological. Human-induced degradation affects 35% of agricultural land, extending cultivation into new marginal areas and increasing intensification on existing croplands [3]. The Mediterranean region is particularly vulnerable to soil degradation [4]; the strongly degraded land area of Western and Central Europe is estimated to be 21 106 ha, which means an 11% of the region [3]. Inappropriate agricultural practices are one of the main driving forces of soil degradation, leading to issues such as erosion, soil fertility loss, SOC decline, salinization, biological degradation, etc., constituting a risk to food security [4,5].
Spain is one of the most affected countries in Europe by high and very high degradation according to the Land Multidegradation Index, with around 30% of the national agricultural area falling under these classes [6]. Combating land degradation is an urgent priority to protect biodiversity and ecosystem services (ESs) that are vital to all life on Earth and to ensure human well-being [1]. ESs can be categorized into provision (nutrition, materials, and energy), regulation and maintenance, and cultural services [7], with soil providing and regulating a large number of ESs, playing a key role in sustaining humanity [8]. According to the State of the World’s Soil Resources Report [9], soil erosion is the most serious threat to soils and soil organic carbon (SOC) reserves, which are critical in the global carbon balance. Non-sustainable land uses, such as intensive conventional tillage, have a negative effect on soil ESs in agricultural areas [8], which leads to a negative impact on productivity in the mid and long term.
Cover crops are one of the sustainable land management (SLM) practices that produce net climate benefits [1] and are one of the most effective SLM practices for erosion control and regeneration of degraded soil [10,11]. Cover crops or groundcovers (GCs) can be employed in woody crops (vineyards, olive groves, almond orchards, etc.) to provide ESs beyond crop production [12]. Furthermore, GCs reduce flooding [13], protect soil against water erosion [14,15], and enhance SOC stock [16,17].
In Mediterranean environments, the reconciliation of cultivation and soil conservation is sometimes difficult [18], particularly in rainfed olive groves and vineyards, which are commonly cultivated on hillslopes and marginal lands [19,20]. Farmers in semiarid areas are reluctant to shift from conventional tillage (CT) to GCs [21,22,23] and they usually expect subsidies, which is understandable given the risk of yield reductions if GCs are not properly managed [22,24].
Since the 1990s, different groups have studied GC in olive groves and vineyards in the Mediterranean area [21,25,26,27], mainly focusing on soil erosion. Research on the role of GC in carbon sequestration started about 20 years ago [28,29,30,31], and in recent years the GC effect in ESs has increased [12,32]. Several of these studies evaluate the impact of GC in the short term (1–3 years), but soil changes take many years, as Koudahe et al. [33] state in their review, and the trials were performed in a few plots under similar soil classes. There is a need to assess the magnitude of physicochemical soil changes related to its management in woody crops in the mid to long term in different soils.
This study aimed to evaluate the mid-term (approximately six years) effects of tillage and alternative soil management practices—GC and no management (NM)—on soil health in olive groves and vineyards located in central Spain. This study was conducted under a semiarid climate and diverse soil conditions using paired plots. To assess the potential benefits of GC and NM compared to CT, thirty-six plots were selected, where flora inventories were conducted, and vegetation coverage and biomass were measured as key drivers of soil changes. The study focuses on evaluating critical soil properties indicative of soil health across various depths, particularly those linked to soil structure, including infiltration rate, penetration resistance, soil organic carbon stock, aggregate stability, bulk density, and porosity. It is hypothesized that the intensity of soil disturbance due to management practices creates a gradient in soil health, with GC providing the most favorable outcomes, followed by NM, and CT resulting in the least favorable outcomes.

2. Materials and Methods

2.1. Study Area

The study area covers the east and southeast of the Community of Madrid (central Spain), in the Las Vegas and Campina agrarian regions (Figure 1), with a total area of 2388 km2. The majority of the territory corresponds to a semiarid Mediterranean climate, with average annual temperatures of 14.5 °C, 473 mm of average annual cumulative rainfall, and 1112 mm of average potential evapotranspiration [34]. In these regions, woody crops are among the most important crops, mainly olive groves followed by vineyards [35].

2.2. Sites Description

In total, 17 sites were selected from 39 locations visited in these regions: 16 were olive groves and 1 vineyard. The selection was based on obtaining the maximum number of sites of woody crops, even with different soils, that had alternative soil management for more than 4 years, and with a neighboring plot with a similar crop that was traditionally tilled. Each site was composed of two neighboring plots under the same edaphoclimatic conditions, one plot managed with an alternative management, named as A, and the other following conventional tillage (2–4 passes per year with a chisel plow), named as B. Figure 2 shows some samples of the plots. Two of the sites were public research farms located at the experimental olive grove center of La Chimenea and the experimental vineyard center of El Socorro, both belonging to the IMIDRA, and the remaining plots belonged to farmers. All the plots in the 17 sites were sampled, with a total of 36 plots, as in 1 location (La Chimenea farm) there were 4 managements in an experimental trial: spontaneous groundcover (O-28A), conventional tillage (O-28B), permanent grass cover of Brachypodium distachyon (O-28C), and annual cover crop of the legume bitter vetch (Vicia ervilia) (O-28D).
Relevant characteristics of the 36 plots are summarized in Table 1. The altitude of sampling points ranged between 535 and 788 MASL, from flat to hilly plots in mainly in loam and basic soils. Most of the olive groves had a traditional framework, with trees older than 15 years. Within the 19 plots of treatment A, 12 were mechanically or chemically mowed (GC; once to three times per year, depending on the plot and the year) and 7 did not need soil management (NM) due to no weeds growing because of depletion in the seed bank. The average age of the alternative managements was 6.2 and 6.0 years for GC and NM treatments, respectively.

2.3. Vegetation Composition and Soil Coverage

During the spring of 2021, a flora inventory was performed at the same time that plant and ground cover (%) were measured in each plot, using quadrats (25 × 25 cm2); the resulting plant cover was the average coverage judged by six trained observers based on four repetitions. The aboveground biomass was then manually cut to ground level with grass shears and oven-dried in the laboratory at 50 °C until constant weight, and then weighted to obtain the dry aboveground biomass.

2.4. Physicochemical Soil Parameters

For each sampling point, four composite samples of soil were obtained randomly to collect 1 kg at four established depths: 0 to 5 cm, 5 to 10 cm, 10 to 20 cm, and 20 to 30 cm. These samples were air-dried and then sieved (2 mm mesh) to remove coarser fractions. The texture in Table 1 was assessed by the Robinson pipette method [36].

2.4.1. Soil Organic Carbon Stock

The soil organic carbon (SOC) content was measured by the Walkley–Black method [37], in which the soil undergoes an oxidation reaction with a standardized potassium dichromate solution. For these measurements, soil samples were previously milled. The bulk density (g cm−3) was determined at each sampling point using the cylinder method [38]. The carbon stock was determined from Formula (1):
SOC Stock = SOC conc. × BD × d × (1 − δ2 mm) × 102
where C Stock is the stock of carbon (Mg ha−1); SOC conc. is the concentration of C (%); BD is the bulk density (Mg m−3); d is the thickness (m); and δ2 mm is the proportion of gravel larger than 2 mm.

2.4.2. Aggregate Stability

Aggregate stability was measured by the water-stable aggregate method (WSA, [39]) at four depths: 0–5, 5–10, 10–20, and 20–30 cm. The WSA method measures the microaggregate resistance to the impact of water saturation. Three sub-samples, each consisting of 10 g of sieved mineral soil (size 0.25–2 mm), were selected per treatment. Aggregate samples were submerged and emerged over a 0.25 mm sieve at 30 oscillations per minute for 3 min. These calculations were corrected for sand content.

2.4.3. Porosity

Three undisturbed topsoil samples per treatment were randomly collected using core stainless cylinders (100 cm3 and 5 cm high) at 0–5 and 5–10 cm depth. The core samples were saturated with water by capillarity in a sandbox (Eijelkamp®) to determine pF between 0 and 2.0 (0.1 to 10 kPa respectively) by successive weight measurements. Water retention between 2 and 4.2 pF (10 and 1500 kPa, respectively) was determined using a progressive drying process with pressure plate extractors [40]. Finally, the samples were completely dried in oven (24 h at 105 °C), corresponding to pF 7 [41].
The relationship between pore size and water retention capacity was established as follows: macropores (>60 μm) corresponded to matric potentials between pF 0 to 1.8; mesopores (60 to 10 μm) to pF values between 1.8 and 2.54; and micropores (<10 μm) to pF > 2.54. Pores smaller than 0.2 μm diameter corresponded to matric potentials higher than pF 4.2. Available water capacity (AWC) was determined by the difference between water volumes at pF 2.54 and 4.2 and the permanent wilting point (PWP) as the volume of water between pF 4.2 and 7.

2.4.4. Infiltration

A small single ring (Ø = 12 cm) was used to assess steady-state infiltration [39], with four repetitions per treatment.

2.4.5. Soil Penetration Resistance

Soil penetration resistance (PR) was assessed employing a hand penetrometer 06.01 (Eijelkamp®). Data were collected at eight random points per treatment in spring. The PR readings were taken at the following soil depths: 2.5, 5, 10, 15, 20, 25, 30, 35, 40, and 45 cm.

2.5. Statistical Analysis

Statistical analyses were performed using SPSS 29 software [42]. Analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) were applied for the statistical analysis of results; mean comparisons were made using the Least Significant Difference (LSD) method at a p < 0.05 level. The data were transformed when necessary before conducting parametric tests. When the homogeneity of variance between groups was not met for the ANOVA analysis, post hoc comparisons were performed using the Games–Howell test. Finally, to analyze the relationships between variables, a correlogram was developed based on significant bivariate Pearson correlations (p < 0.05) and the networks were visualized in Gephi [43] by using the Fruchterman–Reingold layout algorithm. The node diameters were proportional to their mean degrees with weights, the edge thicknesses proportional to the precision, and the color coefficients dependent on the sign of the coefficients (positive in green color and negative in red).

3. Results

3.1. Soil Cover and Aboveground Biomass

Soil cover and, thus, aboveground biomass exhibited significant variation between sites (Figure A1 and Figure A2 in Appendix A). Vegetation cover was significantly larger (Table 2) in alternative soil managements (65.8% in GC and 50.4% in NM on average) compared to CT (20.5%). However, there were two farms with higher vegetation cover in CT than in the alternative soil managements (O-24 and O-26), possibly due to a lower tillage intensity by the farmer.
The aboveground biomass was highest in the GC treatment (77.0 g m−2), followed by NM (43.6 g m−2) and CT (32.6 g m−2), though the differences were not statistically significant (Table 2). Similarly, the average number of total family richness identified per plot was eight in GC and six in NM (Figure 3). The flora composition was primarily dominated by species from the Asteraceae and Poaceae families (Table A1).

3.2. Physical–Chemical Soil Parameters

3.2.1. Bulk Density

There was no significant effect of soil management on bulk density (BD), which remained around 1.43 g cm−3. However, BD was higher at deeper soil depths compared to the 0–5 cm layer (Table 2).

3.2.2. Soil Organic Carbon

The total SOC stock average per treatment and depth is shown in Figure 4 (individual plot results are shown in Figure A3). The total SOC stock at 0–30 cm depth was 33.0, 27.0, and 28.6 Mg ha−1 in GC, NM, and CT, respectively. The main increase in SOC accumulation occurred at 0–5 and 5–10 cm in GC management (3.0 and 1.6 Mg ha−1 of increase compared to CT).
There was a significant effect of both factors on SOC stock and the interaction was statistically significant (Table 2). There was a similar content at 0–5 and 5–10 cm depths in the NM and CT treatments, but a higher content in the upper layers of GC (Figure 5). These differences disappeared beyond 10 cm depth, with a similar content between the three soil managements at 10–20 and 20–30 cm.

3.2.3. Aggregate Stability

Despite the important differences among sites in the percentage of water-stable aggregates (WSAs) shown in Figure A4, significant differences were found between treatments and depths, as can be seen in Table 2. This provides evidence of the strong influence of soil management. There was an improvement in GC compared to CT at 0–5 and 5–10 cm depths, with an increase of around 10 percentage points (Figure 6). The WSA in the NM and CT treatments was very similar at all depths.
Similar to SOC stock, there was a significant interaction between factors in WSA. Figure 7 shows the similar behavior between NM and CT at all depths, and an increase in WSA in GC mainly in the upper soil layers. At the 20–30 cm depth, no differences among the three treatments were found.

3.2.4. Porosity

The different parameters measured regarding soil porosity (FC, PWP, or AWC) were affected by the soil treatment or the two measured depths (Table 2).

3.2.5. Infiltration

The average infiltration rate was nearly twice as high in GC and NM compared to CT (90.5, 105.5, and 59.8 mm h−1, respectively), with statistically significant differences between the alternative soil treatments and CT. The values for each plot are shown in Figure A5.

3.2.6. Penetration Resistance

Figure 8 shows the PR average (N = 8) for all the measured depths for the three soil management types (Figure A6 shows the results per plot). CT exhibited the lowest values at any depth, whilst GC and NM showed similar patterns. An average increase of 150 N cm−2 was measured in the alternative managements compared to CT at all depths.
There was a significant influence of both factors but there was no interaction in PR (Table 2). CT exhibited the significantly lowest value of PR (417 N cm−2), while the values for GC and NM were similar (566 and 540 N cm−2, respectively). PR increased with depth, showing the greatest differences in the upper layers.

3.3. Relationships Between Variables

A correlogram was depicted based on the strength of the variables (Figure 9) derived from Pearson bivariate correlations (p < 0.05) and the Fruchterman–Reingold layout algorithm. The variables related to vegetation (biomass and vegetation cover) are highly positively correlated with SOC, WSA, Infiltration, and BD, but BD has a weak negative correlation with Infiltration and field capacity (FC). SOC was highly positively correlated with almost all other variables, including BD. A moderate positive correlation was detected between PR and Infiltration.

4. Discussion

The intensity of soil disturbance caused by agricultural management practices is expected to influence structural soil properties, often weakening aggregation and reducing soil organic carbon content. These properties are widely recognized as integrative indicators of soil health [44,45,46]. In this study, the three land management practices considered—groundcover (GC), no management (NM), and conventional tillage (CT)—represent a gradient of disturbance, with GC causing the least disturbance and CT the most. It was hypothesized that GC would exhibit the strongest indicators of soil aggregation, such as higher aggregate stability, water–stable aggregates (WSAs), infiltration rates, and porosity, alongside lower values for bulk density and penetration resistance [47]. However, these expected outcomes were not consistently observed.
Land management practices that promote vegetation growth on soils, like GC, significantly enhance soil cover and aboveground biomass [48]. This has a direct and measurable impact on SOC stock, with increases of 7 to 8.3 Mg ha−1 observed under GC management. The benefits of increased SOC are well documented [49,50]. This represents the most evident and anticipated effect of GC management in agricultural soils. Vegetation cover was substantially higher under GC, exceeding threefold that of conventional tillage (CT) and double that of no management (NM), with values of 66%, 50%, and 21%, respectively. Consequently, total SOC stock at the measured depth increased by 4.4 Mg ha−1 under GC compared to CT, with most of the accumulation concentrated in the topsoil. Other authors found similar increases, such as Montanaro et al. [51], with an average of 5 Mg ha−1 in 7 years, or Garcia-Diaz [52], with 3.2 Mg ha−1 in 3 years, but lower than in the meta-analysis performed by Vicente-Vicente [17], with 1.1 and 2.0 Mg ha−1 year−1 in olive groves and vineyards, respectively, or the increase of 15 Mg ha−1 in 8 years measured by Peregrina [53]. Notably, a soil cover exceeding 60% provides significant erosion protection [54], a critical factor in mitigating one of the greatest threats to agricultural lands in Spain [55].
Despite the relatively high percentages of soil cover, the dry aboveground biomass measured in spring was notably low, averaging 77 g m−2 under GC management. This is significantly lower than values reported in other studies, such as 150–350 g m−2 in the least fertile plots [56] or 1.66 t ha−1 in southern Spain [57]. This discrepancy can likely be attributed to the low annual rainfall (300–400 mm year−1) in central Spain during the year preceding sampling, as well as the inherently low fertility of the soils. While aboveground biomass was roughly half of that reported in other studies, mid-term improvements in soil conditions over six years were evident for some variables, though not for all.
Several variables considered in this study showed no significant differences between soil management practices, including porosity, which encompasses macro-, meso-, and microporosity and plays a critical role in water availability. While previous studies have reported effects of soil management on water availability [52], it is important to note that this research included soils with varying textures—a key determinant of porosity and water retention [58]. This deliberate inclusion of different soil types aimed to ensure that any significant differences observed could robustly highlight the influence of management practices on soil health, independent of inherent soil characteristics. These results are likely to be more broadly applicable and well received by land users for practical recommendations [59].
The heterogeneous soil textures in this study appear to mask the effects of land management on water availability, measured as the difference between field capacity (FC) and the permanent wilting point (PWP). In this research, average FC values ranged from 26.4% to 28.8%, while the PWP ranged from 17.6% to 18%, suggesting that land management practices may have limited influence on water availability. Previous studies, such as the review by Hao et al. [60], have also reported weak effects of cover crops on porosity. However, usually, the combined influence of soil texture, SOC, and management practices typically impacts water availability, as highlighted by Johannes et al. [61]. These combined effects require case-by-case analysis, which is currently being undertaken with a larger dataset (under preparation), as to effectively integrate and promote sustainable land management practices, further localized studies are necessary; in site-specific studies, other authors have demonstrated direct effects of management practices on water availability [54].
Similarly, the heterogeneity of soils resulted in no significant differences in bulk density (BD), with an average value of 1.4 g cm−3 across all treatments. However, differences were observed in penetration resistance (PR). High PR values are considered detrimental, as they indicate soil compaction, which can restrict air and water movement within the soil. In this study, CT exhibited the lowest PR due to soil disturbance caused by tillage. However, this was not correlated with improved infiltration; instead, CT showed both low PR and reduced infiltration rates. Notably, infiltration is a more reliable indicator of soil structure, particularly in semiarid regions with low annual rainfall (~400 mm). Under these conditions, prioritizing soil infiltration capacity is crucial. Greater water infiltration under no tillage (NT) compared to conventional tillage (CT) is well documented for long-term management, but in the short term, NT may show similar or lower infiltration rates due to initial soil compaction and insufficient biological activity for stable soil structure development [62,63]. In this study, infiltration rates were significantly higher under GC and NM compared to CT in the mid-term. While GC and NM exhibited a similar BD, both practices resulted in higher infiltration rates, nearly doubling those observed under CT (Table 2). Although infiltration is strongly related to porosity, BD, and PR, in this research, BD and porosity were measured only at 0–5 cm and 5–10 cm depths, whereas PR was assessed from 2.5 to 45 cm. Despite this, differences between treatments for these parameters were relatively small (Figure 7). These relationships were also reflected in the correlogram analysis (Figure 9). The GC treatment, in particular, enhances soil structure throughout the soil profile, thereby increasing both water infiltration and storage capacity [33].
The higher PR values observed in GC and NM are a consequence of a soil settling process, which takes place whenever tillage ceases [64]. In fact, rather than soil compaction, it is a loss of artificial macroporosity created by tillage. This process is independent of the increase in porosity due to an increase in SOC, as was observed by Koudahe et al. [33] in their review. These authors state that long-term accumulation of soil carbon with cover crops may reduce compaction while increasing soil aggregation and water infiltration. The PR was measured in spring in this research, when the soil in the area is close to field capacity. Hao et al. [60] found that this parameter slightly increased compared to the control in the case of loam and silt loam soils, similar to the results in our work, where 14 out of 36 plots had this classification, showing an average PR increase of 150 N cm−2 over the soil profile.
Water-stable aggregates (WSAs) indicate a soil’s ability to resist disturbances such as intense rainfall or tillage [65]. Higher SOC levels are associated with the increased WSA values observed in soils managed under sustainable land managements compared to CTA [66]. The lower WSA values in the surface layers under CT (Figure 5) suggest that continuous tillage disrupts soil aggregates, reducing their resilience to water impacts. The NM treatment exhibits a similar trend to CT, likely due to its history of prolonged tillage and minimal vegetation development, which has limited any improvement in WSA in the upper layers (0–5 cm and 5–10 cm). In contrast, the GC treatment shows clear improvements in WSA within the surface layers (Figure 5), reflecting its positive impact on soil structure. Consequently, soils managed with GC exhibit better soil structural development, higher WSA, and lower erodibility in the topsoil layers compared to the other two management practices. This indicates better soil structural development and lower erodibility [33], reducing the soil erosion risk and promoting better infiltration (Table 2). Pagliai et al. [67] emphasized that continuous conventional tillage drives the formation of surface crusts, due to the decrease in aggregate stability that finally reduces the infiltration, as showed in the present study. These surface crusts could be observed in most of the studied plots subjected to CT.
Previous studies by our team have identified a knowledge gap and a demand for information about GC management, which remains relatively new and underutilized in semiarid regions, largely due to concerns about water and nutrient competition [21]. After six years of implementing sustainable land management practices, or reducing intensive tillage, improvements were observed in key soil health indicators, including SOC, infiltration, water-stable aggregates, and penetration resistance. These variables not only reflect positive changes in soil structure and function but can also serve as effective metrics to monitor and demonstrate the evolution of soil during the initial years of transitioning to sustainable management practices. The findings of this research regarding the varying effects of management practices should be effectively communicated to policymakers and other stakeholders [68] to inform decision-making, regardless of soil type.

5. Conclusions

This study highlights the impact of three land management practices—groundcover (GC), no management (NM), and conventional tillage (CT)—on soil health in semiarid regions. GC demonstrated the strongest improvements in soil structure, with higher soil organic carbon (SOC) stocks (up to 8.3 Mg ha−1), water-stable aggregates (WSAs), and doubling infiltration rates. Vegetation cover under GC exceeded that of CT and NM, enhancing water retention and soil resilience. However, challenging conditions in some plots, such as soil limitations, restricted seed banks, and water scarcity, have led some olive growers in this semiarid area of central Spain to adopt no soil management (NM). While NM improves infiltration rates, it does not significantly enhance other soil health parameters in the mid-term compared to conventional tillage (CT). A slight increase in soil compaction across the profile is observed under GC, a common effect when tillage is abandoned. This compaction, however, can be mitigated over the long term as soil health improves with consistent GC practices. The increased infiltration rate observed under NM and GC treatments is particularly significant in this semiarid region, where rainfall is scarce. Enhanced infiltration reduces rainwater loss through runoff, allowing for more effective utilization of the limited water resources. This improvement supports soil moisture retention, which is crucial for sustaining crop growth and resilience in water-limited conditions.
This is particularly relevant in regions on the verge of aridity, where efficient water use is essential for sustainable agricultural production. Public administrations should encourage olive growers to transition from CT to GC practices to protect and enhance soil conditions, even in areas where natural vegetation does not naturally thrive. Under the 2023–2027 Common Agricultural Policy (CAP), which rewards farmers for adopting voluntary sustainable practices, incentivizing the development of cover crops in olive groves is a promising strategy. In plots lacking natural vegetation, administrations could support olive growers by providing assistance with seeding the rows and, when necessary, fertilizing them to ensure successful establishment.
The long-term effects of groundcover management on soil properties, particularly biological indicators of soil health and yield response, should be further quantified. Such research would provide a solid foundation for the economic and technical support of this Recommended Management Practice, promoting its adoption among olive growers. Additionally, it would highlight the numerous ecosystem services groundcover management offers, including improved soil structure, carbon sequestration, and enhanced water retention, reinforcing its value as a sustainable agricultural strategy.

Author Contributions

Conceptualization, B.S., A.G.-D. and R.B.; methodology, B.S. and R.B.; formal analysis, B.S., J.P.M.-S. and R.B.; investigation, B.S., O.A.-I., R.R., R.B., M.J.N. and A.M.-D.; resources, B.S., A.G.-D. and R.B.; data curation B.S. and R.B.; writing—original draft preparation, B.S.; writing—review and editing, B.S., R.B., M.J.M., J.P.M.-S., A.M.-D., M.J.N., A.G.-D. and J.G.-C.; supervision, B.S., R.B. and M.J.M.; project administration, B.S. and R.B.; funding acquisition, B.S., R.B. and A.G.-D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the regional government Comunidad de Madrid by the project PDR18-ACCION (GO LEÑOSOST).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors want to thank the olive growers of the “las Vegas” and “Campina” agrarian regions, who allowed us to take samples and measures on their farms. Also, the authors want to recognize Alfredo Cuevas, Adrián Borrego, Roberto Saiz, and La Chimenea workers for their technical support.

Conflicts of Interest

Omar Antón-Iruela was employed by the company GRUPOTIMA. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Appendix A

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Figure A1. Percentage of soil covered by vegetation (living or dead, including mosses, lichens, and litter); rocks and bare soil in each site considering the type of soil management practice (A: alternative management, B: conventional tillage, C: permanent seeded grass, and D: annual legume cover crop) measured in spring (N = 4).
Figure A1. Percentage of soil covered by vegetation (living or dead, including mosses, lichens, and litter); rocks and bare soil in each site considering the type of soil management practice (A: alternative management, B: conventional tillage, C: permanent seeded grass, and D: annual legume cover crop) measured in spring (N = 4).
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Figure A2. Dry aboveground biomass measured in spring in each site considering the type of soil management practice (A: alternative management, B: tillage, C: permanent seeded grass, and D: annual legume cover crop) (N = 4).
Figure A2. Dry aboveground biomass measured in spring in each site considering the type of soil management practice (A: alternative management, B: tillage, C: permanent seeded grass, and D: annual legume cover crop) (N = 4).
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Table A1. List of flora species identified in the groundcovers, frequency of appearance in the plot and richness average, maximum and minimum.
Table A1. List of flora species identified in the groundcovers, frequency of appearance in the plot and richness average, maximum and minimum.
FamilySpeciesPlot Frequency
AsteraceaeAnacyclus clavatus1
Artemisa sp.2
Chondrilla juncea2
Dittrichia viscosa1
Helichrysum stoechas1
Rhaponticum coniferum3
Santolina chamaecyparissus1
Silybum marianum2
Staehelina dubia4
Xanthium orientale1
PoaceaeAvena sp.1
Bromus madritensis3
Cynodon dactylon2
Dactylis glomerata1
Stipa tenacissima2
Taraxacum sp.1
BrassicaceaeDiplotaxis erucoides3
Lepidium sp.3
Capsella bursa-pastoris1
AmaranthaceaeAmaranthus sp.1
Salsola kali1
BoraginaceaeEchium vulgare5
Heliotropium supinum2
EuphorbiaceaeEuphorbia serrata3
Euphorbia chamaesyce = Chamaesyce canescens1
FabaceaeAstragalus sp.2
Medicago sp.2
LamiaceaeLamium amplexicaule2
Rosmarinus officinalis2
PlantaginaceaeLlanten sp.1
Veronica hederifolia2
ApiaceaeEryngium campestre1
AsparagaceaeAsparagus acutifolius4
CistaceaeHalimium atriplicifolium7
ConvolvulaceaeConvolvulus arvensis3
GeraniaceaeErodium sp.2
MalvaceaeMalva silvestris3
PapaveraceaeFumaria officinalis1
PolygonaceaeRumex sp.1
RhamnaceaeRhamnus lycioides1
RosaceaeSanguisorba minor6
SolanaceaeSolanum nigrum1
ThymelaeaceaeDaphne gnidium6
UrticaceaeUrtica dioica1
Species richness per plot (Min.–Max.)7 (2–11)
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Figure A3. Soil organic carbon (SOC) stock per depth in each site with the plot under alternative management (A), conventional tillage (B), permanent grass cover (C), and annual legume cover (D) (N = 4).
Figure A3. Soil organic carbon (SOC) stock per depth in each site with the plot under alternative management (A), conventional tillage (B), permanent grass cover (C), and annual legume cover (D) (N = 4).
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Figure A4. Water aggregate stability (WAS) in percentage in each site, with the soil managements and depths (N = 4). GC: groundcover; NM: no management; and CT: conventional tillage.
Figure A4. Water aggregate stability (WAS) in percentage in each site, with the soil managements and depths (N = 4). GC: groundcover; NM: no management; and CT: conventional tillage.
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Figure A5. Infiltration rate (mm h−1) at the different sites with soil managements (N = 4). GC: groundcover; NM: no management; and CT: conventional tillage.
Figure A5. Infiltration rate (mm h−1) at the different sites with soil managements (N = 4). GC: groundcover; NM: no management; and CT: conventional tillage.
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Figure A6. Penetration resistance (N) at the different sites with soil managements (N = 8). GC: groundcover; NM: no management; and CT: conventional tillage.
Figure A6. Penetration resistance (N) at the different sites with soil managements (N = 8). GC: groundcover; NM: no management; and CT: conventional tillage.
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Figure 1. The Community of Madrid in light gray on the map of Spain (a) and sampling points (pink circles) on the Community of Madrid map. (b) A red line marks the “Las Vegas region” and the purple one the “La Campina region”.
Figure 1. The Community of Madrid in light gray on the map of Spain (a) and sampling points (pink circles) on the Community of Madrid map. (b) A red line marks the “Las Vegas region” and the purple one the “La Campina region”.
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Figure 2. Pictures of the pairs of plots in 3 sites: (a) site O-8 (a1: GC, a2: CT), (b) site O-9 (b1: NM, b2: CT), and (c) site O-28 (c1: GC, c2: CT).
Figure 2. Pictures of the pairs of plots in 3 sites: (a) site O-8 (a1: GC, a2: CT), (b) site O-9 (b1: NM, b2: CT), and (c) site O-28 (c1: GC, c2: CT).
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Figure 3. Number of species per family in the alternative managements (GC and NM).
Figure 3. Number of species per family in the alternative managements (GC and NM).
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Figure 4. Total SOC stock for each treatment (GC: groundcover; NM: no management; and CT: conventional tillage) and depth.
Figure 4. Total SOC stock for each treatment (GC: groundcover; NM: no management; and CT: conventional tillage) and depth.
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Figure 5. Least-squares means of SOC stock for the treatments (GC: groundcover; NM: no management; CT: conventional tillage) and depth.
Figure 5. Least-squares means of SOC stock for the treatments (GC: groundcover; NM: no management; CT: conventional tillage) and depth.
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Figure 6. Waterstable aggregates (WSAs) for the treatments (GC: groundcover; NM: no management; and CT: conventional tillage) at different depths.
Figure 6. Waterstable aggregates (WSAs) for the treatments (GC: groundcover; NM: no management; and CT: conventional tillage) at different depths.
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Figure 7. Least–squares means of water–stable aggregates (%) for the treatments (GC: groundcover; NM: no management; and CT: conventional tillage) and depth.
Figure 7. Least–squares means of water–stable aggregates (%) for the treatments (GC: groundcover; NM: no management; and CT: conventional tillage) and depth.
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Figure 8. Penetration resistance (PR) for the treatments (GC: groundcover; NM: no management; and CT: conventional tillage) and depths from 2.5 to 45 cm (N = 8).
Figure 8. Penetration resistance (PR) for the treatments (GC: groundcover; NM: no management; and CT: conventional tillage) and depths from 2.5 to 45 cm (N = 8).
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Figure 9. Fruchterman–Reingold correlogram representation of the Pearson correlation matrix between the main variables. Stronger correlations are represented by thicker lines, with green lines indicating positive correlations and red lines indicating negative correlations. The included variables are bulk density (BD), aboveground biomass (Biomass), vegetation cover, soil organic carbon stock (SOC), water–stable microaggregates (WSA), infiltration rate (Infiltration), field capacity (FC), permanent wilting point (PWP), and penetration resistance (PR).
Figure 9. Fruchterman–Reingold correlogram representation of the Pearson correlation matrix between the main variables. Stronger correlations are represented by thicker lines, with green lines indicating positive correlations and red lines indicating negative correlations. The included variables are bulk density (BD), aboveground biomass (Biomass), vegetation cover, soil organic carbon stock (SOC), water–stable microaggregates (WSA), infiltration rate (Infiltration), field capacity (FC), permanent wilting point (PWP), and penetration resistance (PR).
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Table 1. Relevant characteristics of the studied plots in the Community of Madrid.
Table 1. Relevant characteristics of the studied plots in the Community of Madrid.
FarmO-8AO-8BO-9AO-9BO-11AO-11BO-12AO-12BO-14AO-14BO-16AO-16BO-18AO-18BO-19AO-19BO-22AO-22B
MunicipalityValdelaguaValdelagunaChinchónVillaconejosArganda del ReyArganda del ReyVillarejo de SalvanésVillarejo de SalvanésBrea de TajoBrea de TajoTorres de la AlamedaTorres de la AlamedaTorres de la AlamedaTorres de la AlamedaTorres de la AlamedaTorres de la AlamedaPerales de TajuñaPerales de Tajuña
Woody cropOliveOliveOliveOliveOliveOliveOliveOliveOliveVineyardOliveOliveOliveOliveOliveOliveOliveOlive
Coordinates (ETRS89)466,321, 4,445,833466,276, 4,445,833460,925, 4,441,558460,936, 4,441,509 462,910, 4,457,339462,900, 4,457,359474,605, 4,440,956474,578, 4,440,963486,919, 4,452,958486,905, 4,452,989469,403, 4,470,424469,425, 4,470,393470,012, 4,470,249470,037, 4,470,252470,135, 4,470,064470,176, 4,470,058468,755, 4,456,047468,525, 4,455,991
Altitude (masl)773773687689710703719721762767772782786788778780745743
Slope (%)2–42–48–108–102–42–48–108–106–86–812–1412–146–86–84–64–62–42–4
Plot surface (ha)0.850.760.510.894.672.270.470.52.122.050.190.450.310.140.340.270.460.36
Plantation density1107783100106736411064190014017818510015614390100
Plantation age (years)>300≈70≈7028≈70≈70≈70>40≈5012≈60≈60≈7028≈60>60>75>75
Soil managementGCCTNMCTNMCTGCCTGCCTGCCTGCCTGCCTNMCT
Alternative management age (years)5-5-7-15-15-6-5-5-7-
Soil textureClay LoamSilty Clay LoamLoamClay LoamLoamLoamClay LoamClay LoamClay LoamClay LoamSandy LoamLoamSilt LoamSilt LoamSandy LoamClay LoamClay LoamClay Loam
pH (1:5 H2O)8.38.37.87.97.27.77.77.78.08.48.06.97.37.37.37.28.48.4
Electrical conductivity (µS cm−1) (1:5 25 °C)18515321781445771410383510752212487101632446531530155174
Limestone (%)12.314.426.032.35.44.854.457.82.93.722.99.115.233.823.018.122.47.6
Soil classification (FAO)LeptosolLeptosolGypsisolGypsisolLeptosolLeptosolCalcisolCalcisolLuvisolLuvisolLeptosolLeptosolLuvisolLuvisolLuvisolLuvisolLuvisolLuvisol
FarmO-24AO-24BO-26AO-26BO-27AO-27BO-28AO-28BO-28CO-28DO-31AO-31BO-33AO-33BO-35AO-35BV-1AV1-B
MunicipalityArganda del ReyArganda del ReyPerales de TajuñaPerales de TajuñaPerales de TajuñaPerales de TajuñaColmenar de OrejaColmenar de OrejaColmenar de OrejaColmenar de OrejaCarabañaCarabañaTielmesTielmesArganda del ReyArganda del ReyColmenar de OrejaColmenar de Oreja
Woody cropOliveOliveOliveOliveOliveOliveOliveOliveOliveOliveOliveOliveOliveOliveOliveOliveVineyardVineyard
Coordinates463,754, 4,457,496463,738, 4,457,508470,863, 4,455,477470,875, 4,455,497469,985, 4,455,049 469,977, 4,455,065455,370, 4,435,773 455,370, 4,435,773 455,370, 4,435,773 455,370, 4,435,773 482,350, 4,455,547 482,305, 4,455,578475,982, 4,451,781475,991, 4,451,743464,275, 4,460,893464,349, 4,460,983468,053, 4,442,632 468,053, 4,442,632
Altitude (msnm)700694753752627629535535535535783782767770683683754754
Slope (%)6–86–82–42–425–3025–306–86–86–86–86–86–82–42–46–86–82–42–4
Plot surface1.980.620.640.40.240.419.079.079.079.073.321.710.630.550.781.337.537.53
Plantation density (trees ha−1)11112470139114139238238238238403255591006441664166
Plantation age (years)39186363>100>10013131313>75>75>75>75 2828
Alternative managementNMCTNMCTNMCTGCCTGC—permanent grassGC—annual legumeGCCTGCCTNMCTGCCT
Alternative management age (years)10-7-7-7-7110-4-5-8-
Soil textureClay LoamLoamClay LoamClay LoamLoamLoamLoamLoamLoamLoamLoamClay LoamClay LoamClay LoamClay LoamClay LoamClay LoamClay Loam
pH (1:5 H2O)7.57.68.58.38.58.47.77.77.67.68.08.58.58.58.38.47.78.2
Electrical conductivity (µS cm−1) (1:5 25 °C)5074714561621652052140216021302180195151210164135110540239
Limestone (%)27.016.712.312.74.53.411.59.78.39.67.14.629.529.512.89.713.115.1
Soil classification (FAO)LeptosolLeptosolLuvisolLuvisolLuvisolLuvisolGypsisolGypsisolGypsisolGypsisolLuvisolLuvisolLeptosolLeptosolRegosolRegosolRegosolRegosol
GC: groundcover mechanically/chemically mowed; CT: conventional tillage; and NM: no management.
Table 2. MANOVA (prob > F) and least-squares means of soil properties for the different treatments and depths.
Table 2. MANOVA (prob > F) and least-squares means of soil properties for the different treatments and depths.
Factor
(p-Value)		 Factors
TreatmentDepthTreatment x DepthTreatment Depth
GCNMCT0–55–1010–2020–30
Vegetation cover (%)<0.001--65.8 a50.4 b20.5 c----
Aboveground biomass (g m−2)<0.001--77.0 a43.6 b32.6 b----
BD (g cm−3)0.819<0.0010.1681.44 a1.42 a1.43 a1.32 b1.46 a1.49 a1.45 a
SOC (Mg ha−1)<0.001<0.0010.0028.3 a6.7 b7.1 b6.0 c5.8 c9.7 a8.2 b
WSA (%)<0.001<0.0010.00249.5 a42.1 b43.3 b44.4 b41.4 b45.1 ab49.0 a
FC (%)0.3360.4720.86028.8 a26.4 a28.1 a28.2 a27.4 a--
PWP (%)0.9800.7510.91917.8 a18.0 a17.6 a18.0 a17.5 a--
AWC (%)0.0810.6780.93811.0 a8.4 a10.5 a10.2 a9.8 a--
Infiltration (mm h−1)<0.001--90.5 a105.5 a59.8 b----
PR (N cm−2)<0.001<0.0010.991566 a540 a417 b351 d498 c560 b620 a
GC: groundcover; NM: no management; CT: conventional tillage; BD: bulk density; SOC: soil organic carbon, WSA: water-stable microaggregates; FC: field capacity; PWP: permanent wilting point; AWC: available water content; and PR: penetration resistance. Different letters mean differences between treatments or depths at p < 0.05.
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Sastre, B.; Antón-Iruela, O.; Moreno-Delafuente, A.; Navas, M.J.; Marques, M.J.; González-Canales, J.; Martín-Sanz, J.P.; Ramos, R.; García-Díaz, A.; Bienes, R. Groundcovers Improve Soil Properties in Woody Crops Under Semiarid Climate. Agriculture 2024, 14, 2288. https://doi.org/10.3390/agriculture14122288

AMA Style

Sastre B, Antón-Iruela O, Moreno-Delafuente A, Navas MJ, Marques MJ, González-Canales J, Martín-Sanz JP, Ramos R, García-Díaz A, Bienes R. Groundcovers Improve Soil Properties in Woody Crops Under Semiarid Climate. Agriculture. 2024; 14(12):2288. https://doi.org/10.3390/agriculture14122288

Chicago/Turabian Style

Sastre, Blanca, Omar Antón-Iruela, Ana Moreno-Delafuente, Mariela J. Navas, Maria Jose Marques, Javier González-Canales, Juan Pedro Martín-Sanz, Rubén Ramos, Andrés García-Díaz, and Ramón Bienes. 2024. "Groundcovers Improve Soil Properties in Woody Crops Under Semiarid Climate" Agriculture 14, no. 12: 2288. https://doi.org/10.3390/agriculture14122288

APA Style

Sastre, B., Antón-Iruela, O., Moreno-Delafuente, A., Navas, M. J., Marques, M. J., González-Canales, J., Martín-Sanz, J. P., Ramos, R., García-Díaz, A., & Bienes, R. (2024). Groundcovers Improve Soil Properties in Woody Crops Under Semiarid Climate. Agriculture, 14(12), 2288. https://doi.org/10.3390/agriculture14122288

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