Effects of Cover Crops on Soil Mesofauna in Horticultural Systems in Portugal
1
School of Agriculture, Santarém Polytechnic University, Quinta do Galinheiro—S. Pedro, 2001-904 Santarém, Portugal
2
Research Center in Natural Resources, Environment and Society (CERNAS), Santarém Polytechnic University, Quinta do Galinheiro—S. Pedro, 2001-904 Santarém, Portugal
3
Life Quality Research Center (CIEQV), Santarém Polytechnic University, Complexo Andaluz, Apartado 279, 2001-904 Santarém, Portugal
*
Author to whom correspondence should be addressed.
Horticulturae 2026, 12(4), 408; https://doi.org/10.3390/horticulturae12040408 (registering DOI)
Submission received: 16 February 2026
/
Revised: 21 March 2026
/
Accepted: 23 March 2026
/
Published: 25 March 2026
(This article belongs to the Special Issue Innovative Plant Protection Strategies for Regenerative and Conservation-Based Horticulture Systems)
Abstract
Soil is essential for human survival, with approximately 95% of global food production originating from land. However, over the past century, overexploitation has led to soil degradation and biodiversity loss, with significant impacts on agroecosystems. Portuguese agriculture faces diverse challenges, particularly in the horticultural sector, which occupies substantial territory and supports key economic chains. Consequently, indicators for assessing soil quality are crucial, with mesofauna serving as sensitive bioindicators due to their ecosystemic roles. Among sustainable practices, cover crops are believed to mitigate soil issues by enhancing the biotic functionalities. This study aimed to evaluate the impact of cover crops on soil biological quality in horticultural systems in Portugal. From 2022 to 2025, six horticultural fields in the Alentejo, Ribatejo, and Oeste regions were assessed, introducing cover-crops before main crops and comparing them to controls. Soil samples were collected during cover and main crop presence; mesofauna was extracted via Berlese-Tullgren funnels and classified under the QBS-ar methodology. Results showed enhanced soil biological quality (p < 0.001) in cover crop plots compared to controls, with no significant differences across regions (p = 0.66) or crop types (p = 0.37), indicating the implementation of cover crops as the primary driver for enhanced soil health.
1. Introduction
Soil is one of the most diverse habitats on Earth, estimated to harbor more than a quarter of global biodiversity. This fact stems from its complex physical and chemical nature, offering a wide variety of habitats for a multitude of organisms that promote essential ecosystem services, ranging from the regulation of nutrient, water, and climate cycles to plant growth and food production [1]. However, over the last century, soils have been overexploited through agriculture and industrial development [2], leading to degradation of their quality, with severe impacts on agroecosystems and the environment. This is viewed as a major threat to the future [3], as the consequences of these impacts, combined with climate change-induced alterations, have progressively reduced soils’ potential to sustain human needs, making soil integrity a critical global issue [4,5].
Agricultural activities represent one of the most intensive forms of land use, with impacts depending on adopted management practices. Intensive conventional farming systems have a greater effect on soil biodiversity than low-input conservative practices [2,6]. As a result, several countries are addressing soil decline through conservation strategies, such as integrated pest management, targeted product application, diversified crops, and soil covers [1,7]. Portuguese agriculture is subject to a great diversity of conditions, causing evident economic and environmental difficulties [8], with predominant production systems relying on monoculture, with high energy and production factor inputs, leading to relevant plant protection issues [9]. This results in progressive soil degradation, loss of fertility, and reduced biodiversity [10], highlighting soil health as a major issue in these systems [11].
Thus, assessing different aspects of soil degradation has become a priority in its management and protection. In recent years, various biological indices have been developed, as changes in soil properties can affect its fauna in terms of biodiversity, abundance, and functional relationships. Soils exhibiting good quality in terms of organic matter content, absence of pollution, and disturbances tend to host well-developed and diverse fauna communities [2,12]. Among soil biota, arthropods represent one of the most important community components, involved in various processes, such as organic matter fragmentation, translocation, and decomposition, nutrient cycling, and regulation of resource availability for other species [13], thus forming key links to channel energy from microfauna to macrofauna at higher trophic levels [14]. Additionally, some groups are highly sensitive to changes, as they are extremely adapted to very specific conditions [15].
Among the various indices developed, the QBS-ar relates microarthropod community biodiversity to soil vulnerability degree. It is a metric based on the concept that the number of microarthropod groups morphologically well adapted to the soil is higher in high-quality soils than in low-quality soils. This index combines two important aspects regarding soil microarthropods: (i) their presence in the soil, intended as biodiversity, and (ii) their capability to adapt to soil conditions, intended as vulnerability, providing information on the soil biological quality, which is an indicator of land degradation [15,16]. It has been considered a standard protocol for measuring soil fauna in European ecosystem research programs [17], with several publications reporting its application results so far [2].
Cover crops integrate cultural systems with multiple objectives, such as protection against erosion and nutrient loss, weed vegetation suppression, carbon sequestration, and organic matter addition [18], which improves soil structure and increases water infiltration, nutrient retention, and soil cation exchange capacity [19]. Additionally, cover crops can increase biodiversity [20], as root exudates can attract a variety of organisms near the rhizosphere. The total biomass of soil organisms reflects the level of organic matter input present, so agricultural practices that include regular organic matter inputs in their rotation generally have larger soil communities than conventional agricultural practices [1].
Although cover crops are increasingly promoted for soil health improvement, studies that specifically quantify their effects on soil mesofauna communities using standardized indices such as the QBS-ar remain scarce in Portugal, particularly in horticultural systems, where data are limited to local applications, indicating an absence of national-scale assessments. These systems occupy substantial areas and face challenges related to soil degradation and climatic variability [21], compounded by high disturbance (tillage, short cycles, and high inputs) and low plant cover that strongly affect mesofauna [10,21,22], still remaining unclear how different cover crop mixtures can influence mesofauna abundance, community structure, and soil adaptation levels in these contexts. This uncertainty limits the development of evidence-based recommendations for farmers and restricts the integration of soil biodiversity indicators into national monitoring frameworks. The present study addresses this gap by evaluating the impact of biodiverse cover crops on soil mesofauna communities across six commercial horticultural fields in the Ribatejo, Oeste, and Alentejo litoral regions of Portugal, from 2022 to 2025.
2. Materials and Methods
2.1. Study Sites
The six commercial horticultural fields were located in the Ribatejo (RB1: 39.407° N, 8.453° W; RB2: 39.436° N, 8.482° W; RB3: 39.342° N, 8.505° W; RB4: 39.299° N, 8.913° W), Oeste (OE: 39.063° N, 9.372° W), and Alentejo Litoral (AL: 37.491° N, 8.785° W) regions. The climate in these regions is typically Mediterranean with mild winters and dry summers: Ribatejo (annual avg. temp 15.5 °C, precipitation 650 mm); Oeste (15.8 °C, 700 mm); Alentejo Litoral (16.2 °C, 550 mm) [23].
Prior management to study included conventional horticulture practices, including monoculture and regular tillage. Plot size was approximately 1 ha, with one treatment plot and one adjacent control plot per field (pseudoreplicated design due to commercial constraints). Sampling dates were aligned to each crop cycle to capture both cover crop and main crop phases (Table 1).
Cover crops with biodiverse mixtures (legume–grass associations) were introduced in treatment plots preceding the main crops, while adjacent control plots were maintained with spontaneous vegetation allowed to develop naturally. Cover crops were mowed at maturity and incorporated into the soil at the same time as the spontaneous vegetation using light tillage, with no herbicide applied. After termination of cover crops, the main crops were established across both treatment and control plots. For each sampling period, treatment and control plots were sampled concurrently for comparison. Main soil physical and chemical properties were determined from composite samples collected before the establishment of each trial, prior to cover crop sowing, and are summarized in Table 2. Soil texture was classified according to the USDA system.
2.2. Soil Sampling
Soil samples were collected using a soil probe, with eight random subsamples taken to a depth of 20 cm and combined into one composite sample for each treatment modality, field, and sampling period. Subsamples were gathered at least 10 m from plot boundaries and 5–10 m apart from each other. The composite samples were placed in labeled transparent plastic bags and transported to the laboratory on the same day of collection.
2.3. Microarthropod Extraction
Microarthropods were extracted from soil samples using custom-made Berlese-Tüllgren extractors at room temperature, equipped with 40 W halogen lamps (Avide, Debrecen, Hungary) (ca. 500 lm) positioned 20 cm above the samples, with no added ventilation. Samples (ca. 1 L volume, 10 cm height) were manually disaggregated, then placed in customized 2 mm mesh sieves, directed by funnels into containers with a preservative liquid (70% ethanol, Carlo Erba Reagents S.A.S., Val de Reuil, France). Extractions were performed continuously for seven days.
2.4. EMI Assignment and QBS-ar Index Calculation
Extracted arthropods were observed and photographed under a stereomicroscope (Nikon SMZ800, Nikon, Tokyo, Japan). An Eco-Morphological Index (EMI) value was assigned to all microarthropod groups at the taxonomic resolution (typically class or order level) defined in the EMI assignment table for QBS-ar calculation [2]. Some groups show only one EMI value because all the species belonging to these taxa show the same adaptation level to soil, while other groups show a range in relation to the different adaptation levels of species to soil. In general, edaphic forms obtain EMI = 20, hemiedaphic forms are given an EMI rating proportionate to their degree of soil adaptation, and epigeous forms obtain a score of EMI = 1. Final QBS-ar indices were calculated as the sum of EMI values for each sample [2,15,24].
2.5. Statistical Analysis
QBS-ar data were analyzed statistically using IBM SPSS Statistics 30.0.0.0. To compare treatment modalities within each field (treatment vs. control) and overall, across all fields (combined treatments vs. combined controls), non-parametric Mann–Whitney U tests for independent samples were applied at a significance level of p < 0.05. Kruskal–Wallis tests were used to assess differences across regions and crop types. Effect size was expressed as rank-biserial correlation r (calculated from the Mann–Whitney Z statistic as r = |Z|/√N).
3. Results
The QBS-ar indices were higher under cover crop treatments (T) than in controls (CO), with variations observed across fields and sampling phases. Descriptive statistics of QBS-ar indices by field and treatment are summarized in Table 3.
Cover crop treatment plots showed higher QBS-ar values than the controls in most sampling periods. Median QBS-ar scores in treatment plots ranged from 41 to 66, while control plots ranged from 28 to 45. Cover crop medians were consistently higher, with differences ranging from 1 point (AL) to 21 points (RB1 and RB4). Variability was notable (SD typically 9–23), reflecting natural patchiness in soil mesofauna and small sample sizes per field.
Examination of the specific taxa present showed that several sensitive and highly adapted groups (e.g., Symphyla, Pauropoda) appeared more frequently or exclusively in the treatment plots. EMI values per taxon, QBS-ar indices, and sampling dates are detailed in Table 4, Table 5 and Table 6 for the respective fields.
When analyzed separately by field, significant differences (p < 0.01) were observed in RB2 and RB3, while others were non-significant, as illustrated in the boxplot (Figure 1).
Effect sizes (rank-biserial correlation, r) ranged from 0.37 (RB1 and RB4) to 0.81 (RB3), with r = 0.79 in RB2. However, the aggregated analysis of all six fields demonstrated a significant difference between the combined cover crop treatments and controls (p < 0.001, Mann–Whitney U test). No differences were found across regions (p = 0.66) or crop types (p = 0.37), as determined by Kruskal–Wallis tests.
Representative photographs of the main soil mesofauna taxa with high Eco-Morphological Index (EMI = 20) values are shown in Figure 2.
4. Discussion
The results of this study suggest that cover crops are associated with improved soil habitat quality for microarthropods, although the magnitude of improvement varied among fields. Compared to the broader literature, these findings are consistent with other Mediterranean studies reporting higher QBS-ar scores in plots managed with cover crops. According to these studies [25,26,27], the beneficial effects arise mainly from the continuous provision of living vegetation and organic inputs that enhance soil organic matter content and structure, favoring the presence of more soil-adapted microarthropod forms and resulting in higher QBS-ar scores.
Even if spontaneous vegetation can provide a baseline level of soil cover, the intentionally sown biodiverse mixtures used in this study were associated with higher QBS-ar values. This observation is consistent with literature suggesting that diverse cover crop mixtures can enhance microarthropod communities through greater functional complementarity and resource provision [28]. It also aligns with broader evidence that cover crop mixtures often enhance supporting ecosystem services linked to soil biodiversity more effectively than spontaneous vegetation in several agricultural contexts [29].
Examination of the specific microarthropod taxa present in the soil communities further illustrates the effects of cover crops on soil habitat structure. Groups such as Symphyla and Collembola are recognized as strong bioindicators in Mediterranean agroecosystems, reflecting their preference for environments with higher organic matter content and favorable microhabitats [26]. In field RB3 (2023), the cover crop treatment plot showed the presence of Symphyla, which thrive in less disturbed conditions [30] together with Collembola exhibiting higher morphological adaptation scores (EMI), consistent with the presence of more soil-adapted forms [15,16]. In field OE (2024), the exclusive presence of Symphyla and morphological adaptations in Collembola in cover crop plots aligned with these observations. In contrast, the atypical absence of Acari was recorded in the control plot of OE during the main crop period, potentially reflecting conditions of limited organic matter and food resources [1,31]. Treatment plots in RB4 (2024) contained Pauropoda, a group known for its irregular occurrence and sensitivity to soil disturbance [31,32,33], and in RB1 (2025), Diplopoda specimens were recorded, consistent with improved soil conditions [12,15,34].
These results support the integration of cover crops as a strategy to improve soil resilience and combat its degradation in diverse agricultural contexts. Further research exploring long-term temporal trends and taxon dynamics would provide additional insights into these patterns.
These findings contrast, however, with methodological recommendations to avoid QBS-ar sampling in hot seasons (June/July), due to the possibility of arthropod migration or quiescence to bias results [15,16]. However, in this study, EMI scores remained comparable or even higher during summer samplings (Table 4, Table 5 and Table 6), potentially attributable to edaphoclimatic factors or management practices. This suggests the protocol’s seasonality constraints may not apply in all contexts, allowing QBS-ar to remain reliable for comparing modalities (e.g., treatment vs. control) even in hot periods, warranting context-specific adaptations for accurate assessment.
5. Conclusions
This study evaluated the impact of biodiverse cover crops on soil biological quality in Portuguese horticultural systems using the QBS-ar index. Sampling across six fields in Ribatejo, Oeste, and Alentejo Litoral regions from 2022 to 2025 showed higher QBS-ar indices under cover crop treatments compared to controls, with no differences across regions or crop types, indicating cover crop implementation as the primary driver of soil biological quality improvement.
Cover crop implementation not only demonstrated higher soil biological indices but was also associated with more soil-adapted microarthropod groups, signaling greater biological quality and stability. These findings highlight cover crops’ role in fostering resilient microhabitats, potentially leading to mitigated soil degradation.
However, seasonality constraints in QBS-ar protocols may warrant context-specific adaptations, as summer samplings yielded comparable or higher scores in this study context, despite some studies’ warnings. Other limitations include the smaller sample size for Alentejo and Oeste regions, the pseudoreplicated design of the commercial fields, and the lack of botanical characterization of vegetation; thus, future research should incorporate a more complete assessment together with long-term monitoring to elucidate mechanisms and scalability. Overall, integrating biodiverse cover crops has proven to be a practical strategy for enhancing soil health in Portuguese horticultural systems, supporting their wider adoption as a key measure for environmental resilience.
Author Contributions
Conceptualization, M.D., E.V. and M.G.; methodology, M.D., E.V. and M.G.; software, M.D.; investigation, M.D. and P.C.; data curation, M.D.; writing—original draft preparation, M.D.; writing—review and editing, M.D., E.V., P.C., R.C. and M.G.; supervision, R.C. and M.G.; project administration, R.C. and M.G.; funding acquisition, R.C. and M.G. All authors have read and agreed to the published version of the manuscript.
Funding
This work was funded by the Portuguese Recovery and Resilience Plan (PRR) through the European Union’s NextGenerationEU instrument, under the RedeSusterra (grant agreement no. PRR-C05-i03-I-000093) and Soilife1st (grant agreement no. PRR-C05-i03-I-000006) projects. The authors thank the Foundation for Science and Technology (FCT) for the financial support to the Research Centre for Natural Resources, Environment and Society—CERNAS (UIDB/00681/2025) and to the Life Quality Research Center—CIEQV (FCT/UID/4748/2025, and Forest Research Centre (UID/00239/2025; DOI: 10.54499/UID/00239/2025 and UID/PRR/00239/2025; DOI: 10.54499/UID/PRR/00239/2025).
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 thank the landowners of the six horticultural fields for kindly granting access to their properties. We are especially grateful to Filipe Madeira for his constructive criticism of the manuscript. We also thank the two anonymous reviewers for their valuable comments and suggestions. Finally, we thank Henrique Santos for creating the graphical abstract image.
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
| AL | Alentejo Litoral test field |
| ca. | circa |
| cm | centimeter |
| EMI | Eco-Morphological Index |
| lm | lumen |
| m | meter |
| OE | Oeste test field |
| QBS-ar | Soil Biological Quality Index |
| RB | Ribatejo test fields (RB1–RB4) |
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Figure 1.
Boxplot of QBS-ar indices comparing cover crop (T) and control (CO) plots (2022–2025). Significance codes: ‘**’ p < 0.01; ‘ns’ non-significant (Mann–Whitney U test). ‘*’ indicates outliers.
Figure 1.
Boxplot of QBS-ar indices comparing cover crop (T) and control (CO) plots (2022–2025). Significance codes: ‘**’ p < 0.01; ‘ns’ non-significant (Mann–Whitney U test). ‘*’ indicates outliers.
Figure 2.
Representative soil mesofauna taxa with high Eco-Morphological Index (EMI = 20) values, from cover crop treatment plots, photographed under stereomicroscope. (a) Collembola, (b) Protura, (c) Symphyla, (d) Pauropoda, (e) Acari. Red scale bars = 0.5 mm.
Figure 2.
Representative soil mesofauna taxa with high Eco-Morphological Index (EMI = 20) values, from cover crop treatment plots, photographed under stereomicroscope. (a) Collembola, (b) Protura, (c) Symphyla, (d) Pauropoda, (e) Acari. Red scale bars = 0.5 mm.
Table 1.
Summary of the six test fields, including region, main crop type, and sampling dates (2022–2025).
Table 1.
Summary of the six test fields, including region, main crop type, and sampling dates (2022–2025).
| Field | Region | Main Crop | Sampling Periods 1 |
|---|---|---|---|
| RB1 | Ribatejo | Maize | Feb (cc); Jun, Jul (mc) for 2024–2025 |
| RB2 | |||
| RB3 | Tomato | Feb (cc); Jun, Jul (mc) for 2022–2025 | |
| RB4 | |||
| OE | Oeste | Broccoli | Feb (cc); Jun, Jul (mc) for 2024–2025 |
| AL | Alentejo | Lettuce | Feb (cc); May (mc) for 2023–2025 |
1 Sampling dates varied based on field availability and crop rotation schedules. (cc: cover crop presence; mc: main crop presence).
Table 2.
Main physicochemical properties of the soils in the six experimental fields, determined prior to trial establishment (mean values). Soil texture is classified according to the USDA system.
Table 2.
Main physicochemical properties of the soils in the six experimental fields, determined prior to trial establishment (mean values). Soil texture is classified according to the USDA system.
| Field | Texture | pH | Organic Matter (%) | Assimilable P (mg kg−1) | Assimilable K (mg kg−1) |
|---|---|---|---|---|---|
| RB1 | Clay loam | 7.9 | 1.1 | 389 | 164 |
| RB2 | Clay loam | 5.7 | 2.1 | 101 | 189 |
| RB3 | Loam | 8.1 | 1.0 | 226 | 90 |
| RB4 | Loam | 7.7 | 1.6 | 500 | 312 |
| OE | Sandy loam | 6.3 | 1.0 | 98 | 220 |
| AL | Sandy loam | 7.6 | 1.4 | 291 | 91 |
Table 3.
Descriptive statistics (median, mean ± SD, range) of QBS-ar indices by field and treatment, pooled across all sampling phases (n = 6 per treatment).
Table 3.
Descriptive statistics (median, mean ± SD, range) of QBS-ar indices by field and treatment, pooled across all sampling phases (n = 6 per treatment).
| Field | Modality | Median | Mean ± SD | Range (Min–Max) |
|---|---|---|---|---|
| RB1 | T | 66 | 63.7 ± 23.0 | 30–87 |
| CO | 45 | 45 ± 18.2 | 18–71 | |
| RB2 | T | 56 | 55.8 ± 4.3 | 51–61 |
| CO | 41 | 40.5 ± 6.7 | 30–51 | |
| RB3 | T | 42 | 46.5 ± 11.6 | 40–70 |
| CO | 28 | 28.8 ± 6.1 | 22–40 | |
| RB4 | T | 61 | 55 ± 15.9 | 30–71 |
| CO | 40 | 44.2 ± 11.1 | 32–62 | |
| OE | T | 56 | 50.8 ± 18.1 | 20–71 |
| CO | 39 | 33.3 ± 18.2 | 11–51 | |
| AL | T | 41 | 46.3 ± 9.1 | 40–60 |
| CO | 40 | 39.8 ± 9.6 | 28–51 |
T: treatment plot; CO: control plot.
Table 4.
EMI values and QBS-ar indexes, for the Maize test fields in the Ribatejo region.
| Taxa | RB1 | RB2 | ||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2024 | 2025 | 2024 | 2025 | |||||||||||||||||||||
| Feb | Jun | Jul | Feb | Jun | Jul | Feb | Jun | Jul | Feb | Jun | Jul | |||||||||||||
| T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | |
| Acari | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | |
| Araneae | - | - | - | - | - | - | - | - | - | 5 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Chilopoda | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 20 | - | - | - | - | - | 10 | - | 10 | - |
| Collembola | 10 | 10 | 10 | 8 | 20 | 10 | 10 | 8 | 20 | 10 | 20 | 10 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 10 | 20 | 20 | 20 | 10 |
| Coleoptera | - | 5 | - | - | 15 | 1 | 1 | - | 1 | 1 | 1 | 1 | 5 | - | - | - | 1 | 1 | 1 | - | - | - | - | - |
| Coleoptera (lv.) | - | 10 | - | - | 10 | - | 10 | - | 10 | - | 10 | 10 | 10 | - | - | - | 10 | - | 10 | - | 10 | 10 | - | 10 |
| Diplopoda | - | - | - | - | - | - | 10 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Diptera | 1 | - | - | - | 1 | - | - | - | 1 | 1 | - | - | - | 1 | - | - | - | - | - | - | - | 1 | 1 | - |
| Diptera (lv.) | 10 | 10 | - | - | - | - | - | 10 | 10 | - | 10 | 10 | - | - | - | - | - | - | - | - | - | - | - | - |
| Hemiptera | - | - | - | - | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Himenoptera | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 5 | - | - | - | - | - | - | - |
| Other (lv.) | 10 | - | - | 10 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Pauropoda | - | - | - | - | - | - | - | - | - | - | 20 | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Psocodea | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 1 | - | - | - | 1 | - | - | - |
| Protura | - | - | - | - | 20 | 20 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Symphyla | - | - | - | - | - | 20 | - | - | 20 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| QBS-ar | 51 | 55 | 30 | 18 | 87 | 71 | 51 | 38 | 82 | 37 | 81 | 51 | 55 | 41 | 60 | 40 | 57 | 41 | 51 | 30 | 61 | 51 | 51 | 40 |
T: treatment plot; CO: control plot; (lv.): larvae; (-): not present.
Table 5.
EMI values and QBS-ar indexes, for the Tomato test fields in the Ribatejo region.
| Taxa | RB3 | RB4 | ||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022 | 2023 | 2024 | 2025 | |||||||||||||||||||||
| Feb | Jun | Jul | Feb | Jun | Jul | Feb | Jun | Jul | Feb | Jun | Jul | |||||||||||||
| T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | |
| Acari | 20 | 20 | 20 | 20 | 20 | - | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 |
| Araneae | - | 5 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Collembola | 10 | 2 | 10 | 4 | 10 | 10 | 10 | 8 | 20 | 10 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 10 | 20 | 10 | 10 | 10 | 10 | 8 |
| Coleoptera | 5 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 1 | 1 | - | 1 | - | - | - | - |
| Coleoptera (lv.) | 10 | - | 10 | - | 1 | - | - | - | - | - | - | - | - | 10 | - | - | 10 | 10 | 10 | - | - | 10 | 10 | 10 |
| Diptera | - | - | 1 | 1 | 1 | 1 | 1 | - | - | - | - | - | 1 | 1 | 1 | - | - | 1 | 1 | 1 | - | - | 1 | - |
| Diptera (lv.) | - | - | - | - | - | - | 10 | - | - | - | - | - | - | - | 10 | - | - | - | 10 | - | - | - | - | - |
| Hemiptera | - | 1 | - | - | - | 1 | - | - | - | - | - | - | - | - | - | - | - | 1 | - | - | - | - | - | - |
| Himenoptera | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 5 | - | - | - | - | - |
| Other (lv.) | - | - | - | - | 10 | 10 | - | - | - | - | 10 | - | - | 10 | 10 | - | - | 10 | - | - | - | - | - | - |
| Pauropoda | - | - | - | - | - | - | - | - | - | - | - | - | 20 | - | - | - | - | - | - | - | - | - | - | - |
| Symphyla | - | - | - | - | - | - | - | - | - | - | 20 | - | - | - | - | - | 20 | - | - | - | - | - | - | - |
| Thysanoptera | - | - | - | - | - | - | - | - | - | - | - | - | - | 1 | - | - | - | - | - | - | - | - | - | - |
| QBS-ar | 45 | 28 | 41 | 25 | 42 | 22 | 41 | 28 | 40 | 30 | 70 | 40 | 61 | 62 | 61 | 40 | 71 | 53 | 66 | 32 | 30 | 40 | 41 | 38 |
T: treatment plot; CO: control plot; (lv.): larvae; (-): not present.
Table 6.
EMI values and QBS-ar indexes, for the Broccoli and Lettuce test fields in the Oeste and Alentejo litoral regions.
Table 6.
EMI values and QBS-ar indexes, for the Broccoli and Lettuce test fields in the Oeste and Alentejo litoral regions.
| Taxa | OE | AL | ||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2024 | 2025 | 2023 | 2024 | 2025 | ||||||||||||||||||||
| Feb | Jun | Jul | Feb | Jun | Jul | Feb | May | Feb | May | Feb | May | |||||||||||||
| T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | T | CO | |
| Acari | 20 | 20 | 20 | - | 20 | - | - | - | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 |
| Araneae | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Chilopoda | - | - | - | - | - | 20 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Collembola | 20 | 20 | 20 | 10 | 20 | 10 | 20 | 10 | 20 | 20 | 10 | 10 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 10 | 20 | 8 |
| Coleoptera | - | - | 1 | - | - | 1 | - | - | - | - | 1 | 1 | - | - | - | - | 5 | - | - | - | - | - | - | - |
| Coleoptera (lv.) | - | - | - | - | - | 10 | - | - | 10 | 10 | 10 | - | 10 | 10 | - | - | - | - | - | - | - | - | - | - |
| Diptera | 1 | 1 | - | 1 | - | - | - | 1 | 1 | - | - | - | - | - | 1 | - | 1 | 1 | - | - | - | - | - | - |
| Diptera (lv.) | - | - | - | - | 10 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Hemiptera | - | - | - | - | - | - | - | - | 1 | - | - | - | - | - | - | - | - | - | 1 | - | - | - | - | - |
| Himenoptera | - | - | - | - | - | - | - | - | - | - | - | 5 | - | - | - | - | - | - | - | - | - | - | - | - |
| Other (lv.) | 10 | 10 | - | - | 10 | - | - | - | - | - | - | - | 10 | - | - | - | 10 | 10 | - | - | - | - | - | - |
| Symphyla | 20 | - | - | - | - | - | - | - | - | - | 20 | - | - | - | - | - | - | - | - | - | - | - | - | - |
| QBS-ar | 71 | 51 | 41 | 11 | 60 | 41 | 20 | 11 | 52 | 50 | 61 | 36 | 60 | 50 | 41 | 40 | 56 | 51 | 41 | 40 | 40 | 30 | 40 | 28 |
T: treatment plot; CO: control plot; (lv.): larvae; (-): not present.
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MDPI and ACS Style
Duarte, M.; Valério, E.; Cardoso, P.; Coelho, R.; Godinho, M. Effects of Cover Crops on Soil Mesofauna in Horticultural Systems in Portugal. Horticulturae 2026, 12, 408. https://doi.org/10.3390/horticulturae12040408
AMA Style
Duarte M, Valério E, Cardoso P, Coelho R, Godinho M. Effects of Cover Crops on Soil Mesofauna in Horticultural Systems in Portugal. Horticulturae. 2026; 12(4):408. https://doi.org/10.3390/horticulturae12040408
Chicago/Turabian StyleDuarte, Mário, Elsa Valério, Pedro Cardoso, Rosa Coelho, and Maria Godinho. 2026. "Effects of Cover Crops on Soil Mesofauna in Horticultural Systems in Portugal" Horticulturae 12, no. 4: 408. https://doi.org/10.3390/horticulturae12040408
APA StyleDuarte, M., Valério, E., Cardoso, P., Coelho, R., & Godinho, M. (2026). Effects of Cover Crops on Soil Mesofauna in Horticultural Systems in Portugal. Horticulturae, 12(4), 408. https://doi.org/10.3390/horticulturae12040408
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