# Effects of agricultural practices and crops on soil fauna and soil properties in tropical agroecosystems

Gladys Loranger-Merciris, Aurélie Jean-Pierre, Valérie Angeon, Harry

Ozier-Lafontaine

► To cite this version:

Gladys Loranger-Merciris, Aurélie Jean-Pierre, Valérie Angeon, Harry Ozier-Lafontaine. Effects of agricultural practices and crops on soil fauna and soil properties in tropical agroecosystems. Pedobiologia, 2025, 112 (151077), 10.1016/j.pedobi.2025.151077. hal-05213727

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# Effects of agricultural practices and crops on soil fauna and soil properties in tropical agroecosystems

Gladys Loranger-Merciris<sup>a, *</sup>, Aurélie Jean-Pierre<sup>a</sup>, Valérie Angeon<sup>b</sup>, Harry Ozier-Lafontaine<sup>c, 1</sup>

## ARTICLE INFO

**Keywords:**

Caribbean region

Macrofauna

Microarthropods

Soil ecological properties

Tropical crops

## ABSTRACT

At the interface with the other compartments of the earth, soil performs many vital functions for the planet and its inhabitants and is subject to current global changes. Today, more than ever, soil and its management are at the heart of many issues (food security, climate change, environment, health...) and maintaining its biodiversity through appropriate agricultural practices is a key challenge. The overall objective of this study was to assess soil fauna abundance and richness across different tropical cropping systems (intensive versus agroecological) and crop type (tomato, yam, pasture). We assumed that (i) agroecological practices will increase soil fauna abundance and taxonomic richness; (ii) agroecological practices will positively affect the ability of soil fauna to influence soil ecological properties, such as soil aggregation and chemical properties. Our results showed that the abundance and richness of soil macrofauna increased in the agroecological system compared to the conventional system. Our study also showed that crop type influenced soil fauna and should be taken into account to better understand the impact of agricultural practices on soil ecological properties.

## 1. Introduction

Soils in natural and managed ecosystems generate a wide range of functions that support the provision and regulation of various ecosystem services, including food provisioning, water retention, climate regulation, erosion control, carbon sequestration, nutrient cycling, and pest regulation (Adhikari and Hartemink, 2016). Soils exhibit a remarkable biodiversity, characterized by the abundance, diversity, and functionality of microorganisms and fauna (Bardgett and van der Putten, 2014). The activities of these organisms are essential to the provision of soil ecological functions and key ecosystems services (Bardgett and Van der Putten, 2014; Adhikari and Hartemink, 2016; Brussaard, 2021).

Agriculture, as a human activity that manages ecosystems, has an impact on soil quality. Projections indicate that the global population will reach approximately 10 billion by the year 2050, thereby increasing the demand for agricultural products (FAO, 2017). A principal challenge in contemporary agriculture is to ensure food security for an ever-growing population while preserving the quality of the

environment.

As a matter of fact, the intensification of land use has had profound and lasting impacts on the natural environment, particularly on the biodiversity of terrestrial ecosystems, and has altered the ecosystem services they are able to provide in their natural state (Chapin et al., 2000; Tamburini et al., 2020; Lavelle et al., 2022). Intensive practices, such as the addition of pesticides, have been identified as one of the primary causes of soil biodiversity loss. Regular use of pesticides during the growing season has been shown to kill soil organisms directly or indirectly through changes in the environment caused by the chemicals (Voronova, 1968; Bengtsson, 2002; Beaumelle et al., 2023). Regarding mineral fertilizers, recent literature on their effects on soil fauna is contradictory (Hu et al., 2022; Zhu et al., 2023). These results suggest that the effects of different fertilizer types on soil faunal communities are likely to differ between taxonomic groups. Additionally, intensive tillage compared to reduced tillage practices has been shown to have a strongly negative impact on soil communities (Fragoso et al., 1997; Clermont-Dauphin et al., 2004). It is crucial to emphasize that the

<footnote><sup>a</sup> ECOTERCA - ÉCOlogie TERrestre Caribéenne, Université des Antilles, Faculté des Sciences Exactes et Naturelles, Campus de Fouillole, BP 592, Pointe-à-Pitre Cedex 97159, France</footnote>

<footnote><sup>b</sup> UR Ecodéveloppement, INRAE, Avignon 84914, France</footnote>

<footnote><sup>c</sup> ASTRO Agrosystèmes tropicaux, INRAE, Guadeloupe, Petit-Bourg 97170, France</footnote>

<footnote>* Correspondence to: ECOTERCA - ÉCOlogie TERrestre Caribéenne, Université des Antilles, UFR Sciences Exactes et Naturelles, Campus de Fouillole, Guadeloupe, Pointe-à-Pitre Cedex 97157, France.</footnote>

<footnote>E-mail address: gladys.loranger@univ-antilles.fr (G. Loranger-Merciris).</footnote>

<footnote n="1">¹ present address: Parc National de la Guadeloupe, Montéran 97120 Saint-Claude (Guadeloupe)</footnote>
impact of these practices varies depending on the habitat type and the soil taxa under study (Phillips et al., 2024). Soil organisms also influence physical properties like soil aggregation, as well as chemical properties such as nutrient availability and organic matter decomposition (Tisdall et al., 1982; Lee et al., 1991; Zhang et al., 1993; Jouquet et al., 2006). In intensive agricultural systems, environmental stressors such as use of pesticides and tillage negatively affect the ability of soil fauna to influence soil aggregation and chemical properties. This can lead to reduced nutrient availability and disrupted soil functions (Phillips et al., 2024).

In recent years, there has been a movement to adopt alternative cropping systems that share the same goals of minimizing or eliminating the use of synthetic inputs, increasing organic matter inputs, and improving the health of agroecosystems while maintaining high levels of production (Altieri, 1999). However, in these novel systems, it is imperative to elucidate the interactions between plant communities and soil organism communities and to assess their impacts on soil functioning and the provision of ecosystem services. A comprehensive understanding of the intricate relationships between cropping systems and belowground diversity is imperative for enhancing agricultural productivity, particularly in the context of the escalating demand for food security (Domínguez et al., 2018).

In tropical regions, high-input cropping systems are increasingly subject to criticism regarding their environmental impact, which includes soil degradation and pollution (Picq et al., 1999; FAO, 2019). The agricultural sector in these regions is confronted with significant economic, social, and environmental challenges, which are further compounded by climate change (Soumaré et al., 2021). This underscores the necessity of exploring alternative crop production systems to bolster the resilience of these economies by promoting sustainable utilization of natural resources and ensuring food security (FAO, 2019; Soumaré et al., 2021). The Caribbean region, including Guadeloupe, is particularly vulnerable to these challenges. Agroecology has been identified as a pivotal approach to sustaining food security while preserving nature and the ecosystem services it can provide. However, research on tropical cropping systems and their ecological impacts remains limited in comparison to that conducted on temperate regions, underscoring an urgent need for increased research in this area.

The objective of this study is to enhance comprehension of the impact of tropical cropping systems on soil ecological properties, with a focus on the case of Guadeloupe, a Caribbean island. The agricultural landscape of Guadeloupe is characterized by a great diversity of production systems, in line with the history of the territory (agricultural dualism: mono-production for export and local crops for the domestic market) and the bioclimatic specificities of island systems in a humid tropical environment. Another feature of the Guadeloupean territory that makes it an ideal laboratory for studying the intertropical zone is the great diversity of soil types, representative of the main tropical agricultural zones (Sierra et al., 2015). The overarching objective of our study is to assess the biological diversity of soil fauna in different tropical cropping systems and to establish a correlation between changes in faunal diversity and agronomic practices (intensive versus agroecological), crop types (tomato, yam, or pasture) and soil ecological properties. The central hypothesis guiding this study is that crop type and intensification affect soil biodiversity and soil ecological properties. The specific hypotheses to be tested are as follows (i) agroecological practices will increase soil fauna abundance and taxonomic richness; (ii) agroecological practices will positively affect the ability of soil fauna to influence soil ecological properties, such as soil aggregation and chemical properties.

# 2. Materials and methods

## 2.1. *Pedoclimatic properties of the sites*

The study was conducted in Guadeloupe, French West Indies, located in the Caribbean. Covering an area of 1628 km², Guadeloupe is an

archipelago comprising two main islands with distinct environments. Basse-Terre (848 km²) is dominated by a mountain range that runs from north-west to south-east. This island is characterized by a humid tropical climate and the soils are mostly ferralsols (Sierra et al., 2015). Annual rainfall in Basse-Terre ranges from 1400 to 3500 mm.year⁻¹ (French Meteorological Service, http://www.meteo.gp). In contrast, Grande-Terre (586 km²) is characterized by a slightly undulating surface and the relief rarely exceeds 40 m (Sierra et al., 2015). The island has a tropical climate and the soils are mostly vertisols. Annual rainfall ranges from 1300 to 1600 mm.year⁻¹ (French Meteorological Service, http://www.meteo.gp). The annual temperature of the archipelago ranges from 20.1 to 31.9°C.

## 2.2. *Farmers plots*

Three crops were compared: a short-cycle crop (tomato), a semi-perennial crop (yam), and a so-called perennial crop (pasture). These three crops are all destined for the local market. For each crop, agroecological and conventional intensive plots were selected. The inclusion of soil type (Vertisol and Ferralsol) as a random effect in the statistical analysis allows us to account for its potential confounding effects. However, this approach does not allow us to test the specific influence of soil type. A larger sampling effort with a balanced representation of each soil type would be required to robustly assess soil type effects and interactions (Fig. 1). Eighteen farmer plots were chosen: six plots of pasture (perennial crop), including three agroecological plots and three intensive plots; six plots of yam (semi-perennial crop), including three agroecological plots and three intensive plots; and six plots of tomato (short-cycle crop), including three agroecological plots and three intensive plots (Fig. 2). The level of intensification was defined according to the level of external inputs. Plots with low pesticide inputs and no synthetic fertilizers were classified as 'agroecological', whereas plots with high inputs (synthetic fertilizers and pesticides) were classified as 'intensive' (Table 1). It should also be noted that tillage intensity is indirectly taken into account: crop duration is linked to tillage frequency. Five samples were taken for soil chemical and biological characteristics in each plot, for a total of 90 samples.

## 2.3. *Soil biological and physico-chemical characteristics*

In each plot, five soil samples measuring 25 cm (length) × 25 cm (width) × 20 cm (depth) were collected for soil macrofauna extraction according to the ISO 23611-S method (ISO, 2011). Each sampling point was 30 m apart. Macro-organisms were collected in alcohol, enumerated and identified to taxonomic level (Table S1) under a dissecting microscope (Nikon SCOP-PRO, MODEL C-LEDS). Soil macroinvertebrates were classified into 36 taxonomic groups, mainly at the class or order level. In addition, given the diversity of this group, we have classified the Coleoptera at the family level. The functional group of "ecosystems engineers" (ants, earthworms and termites) was also considered (Turbé et al., 2010).

Near the macrofauna samples, five soil samples, 6.5 cm in diameter and 15 cm deep, were taken from each plot for soil mesofauna extraction. Mesofauna were collected in alcohol using the Berlese extraction method, counted and identified to taxonomic level (Table S1) under a dissecting microscope. The organisms were classified into 33 taxonomic groups, mainly at the class or order level. Due to the abundance of this group, Acari (mites) were classified into the suborders Oribatida, Mesostigmata, Prostigmata and Astigmata. Collembola (springtails) were classified at the family level of Entomobryidae, Poduridae, Sminthuridae and Neelidae, also due to their high abundance.

Five other soil samples, 6.5 cm in diameter and 15 cm deep, were taken from each plot for chemical analyses. These soil samples were analyzed for total N using the Dumas method. Total C was measured according to NF ISO 10694. pH-H2O and pH-KCl were measured according to NF ISO 1770, 3696 and 11464.
<figure><img src="image_3.png" /><figcaption>Fig. 1. Schematic representation of the experimental design. I: Intensive; A. Agroecological; V: Vertisol; F: Ferralsol.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 2. Location of the 18 selected plots that were surveyed in Guadeloupe including 6 pasture plots (3 agroecological, 3 intensive), 6 yam plots (3 agroecological, 3 intensive) and 6 tomato plots (3 agroecological, 3 intensive).</figcaption></figure>

The morphological assessment of soil macro-aggregates provides an integrative index of soil biological activity. An undisturbed soil sample using an 8 cm diameter × 8.5 cm height cylinder was taken at the center of each of the 18 farmer plots. In the laboratory, blocks were gently separated and the samples were air-dried then sieved at 2 mm. The soil retained by the sieve was placed on a filter paper and the various elements were separated according to their origin and shape. The elements were sorted according to categories: i) biogenic aggregates of rounded forms, created by macroinvertebrates, ii) physical aggregates of angular forms, produced by the physical processes of the environment (especially alternating dry and wet periods), iii) non-macro aggregated soil (soil passing through the 2 mm sieve), and iv) other components (roots, stones, litter, wood...). The separated samples were put in an oven at 60 °C for 15 days and weighed (Velasquez et al., 2007).

## 2.4. Statistical analysis

Generalized linear mixed models (GLMM; Bolker et al., 2009) were

used to assess the effects intensification of farming practice (Agroecological vs Intensive), crop type (Pasture vs Yam vs Tomato) and their interaction on soil fauna abundance (macroarthropods and microarthropods), taxonomic richness, and on soil physico-chemical properties. The latter included pH-H₂O and pH-KCl, total N and C content, percentage of biological aggregates, non-macro-aggregated soil and physical aggregates. All models included soil type as a random effect. For faunal abundance and richness, Poisson, zero-inflated Poisson, negative binomial, zero-inflated negative binomial, and gamma distributions were tested, with the negative binomial distribution (log-link) providing the best fit. For soil physicochemical properties, the gamma distribution (log-link function) was the most appropriate. Model development was performed using the glmmTMB package (version 1.0.0; Brooks et al., 2017), and all potential covariate combinations, including interactions, were explored using the MuMln package (version 1.43.6; Barton and Barton, 2019). This resulted in five candidate models. Model selection was based on the Akaike information criterion corrected for small sample size (AICc), calculated using the AICcmodavg package (version 2.3–1; Mazerolle and Mazerolle, 2017). Models with ΔAICc < 2 were considered equally plausible in explaining the observed variability in soil faunal abundance (Burnham et al., 2011). Goodness of fit for candidate models was assessed using the DHARMA package (version 0.2.7; Hartig, 2020). Model averaging was applied using the AICcmodavg package to calculate standardized model-averaged estimates (β ± SE) and 95 % confidence intervals [95 % CI]. Predictors with confidence intervals not overlapping zero were considered significant. Predictor estimates were obtained by averaging their values across the subset of models in which the predictor was retained, to avoid shrinking the estimate towards zero (Harrison et al., 2018). In addition, we calculated the marginal R² (R²m), which represents the proportion of variance explained by the fixed effects alone, and the conditional R² (R²c), which reflects the variance explained by both the fixed and random effects. These values were obtained using the r.squaredGLMM function from the MuMln package.

Separate GLMMs were used to assess the effects of macroarthropod abundance and its interactions with crop type and intensification (MA × crop; MA × intensification) on soil aggregation, total N and pH-KCl. A total of 13 candidate models were tested for each response variable. pH-H₂O and total C were excluded from the analysis due to high collinearity with pH-KCl (rs = 0.97) and total N (rs = 0.83) as shown by Spearman's rank correlation tests (p < 0.0001). To avoid multicollinearity and ensure model parsimony, we retained only total macrofauna abundance as a predictor variable of faunal activity. This choice was supported by its stronger and more consistent correlations with soil functions
<table><caption>**Table 1**

Description of the 18 selected farmers plots (Guadeloupe, FWI).</caption><thead><tr><th>Plot</th><th>Crop*Intensification</th><th>Herbicides</th><th>Pesticides</th><th>Mineral fertilization</th><th>Organic fertilization</th></tr></thead><tbody><tr><td>PB</td><td>Pasture*Agroecological</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>PI</td><td>Pasture*Intensive</td><td>-</td><td>-</td><td>N<sub>15</sub>P<sub>15</sub>K<sub>15</sub>, 1 time/year</td><td>-</td></tr><tr><td>PG</td><td>Pasture*Intensive</td><td>-</td><td>-</td><td>N<sub>15</sub>P<sub>15</sub>K<sub>15</sub>, 1 time/year</td><td>-</td></tr><tr><td>PNI</td><td>Pasture*Agroecological</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>PIF</td><td>Pasture*Intensive</td><td>-</td><td>-</td><td>N<sub>15</sub>P<sub>15</sub>K<sub>15</sub>, 1 time/year</td><td>-</td></tr><tr><td>PBIO</td><td>Pasture*Agroecological</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>IGIA</td><td>Yam*Intensive</td><td>Touchdown (glyphosate), 4 times/cycle</td><td>Cythrine®Max, 5 times/cycle</td><td>SK, NS, MAP, 3 times/cycle</td><td>-</td></tr><tr><td>IGIB</td><td>Yam*Intensive</td><td>Basta®(glufosinate ammonium), 2 times/cycle</td><td>-</td><td>-</td><td>-</td></tr><tr><td>IGBC</td><td>Yam*Agroecological</td><td>-</td><td>-</td><td>-</td><td>manure</td></tr><tr><td>IGBD</td><td>Yam*Agroecological</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>IGIE</td><td>Yam*Intensive</td><td>Basta®, 2 times/cycle</td><td>-</td><td>DAP, NPK, 2 times/cycle</td><td>-</td></tr><tr><td>IGBF</td><td>Yam*Agroecological</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>TBA</td><td>Tomato*Agroecological</td><td>-</td><td>-</td><td>-</td><td>manure</td></tr><tr><td>TIB</td><td>Tomato*Intensive</td><td>Cosmic® (glyphosate), 2 times/cycle</td><td>Dimethoate, 1 time/cycle</td><td>DAP, 6 times/cycle</td><td>-</td></tr><tr><td>TIC</td><td>Tomato*Intensive</td><td>Biathlon®(tritosulfuron), 1 time/cycle</td><td>Vermitech®, Dipel, Dithane™</td><td>DAP, 6 times/cycle</td><td>-</td></tr><tr><td>TID</td><td>Tomato*Intensive</td><td>Basta®, 1 time/cycle</td><td>Dipel</td><td>DAP, NPK, 4 times/cycle</td><td>manure</td></tr><tr><td>TBE</td><td>Tomato*Agroecological</td><td>-</td><td>-</td><td>-</td><td>manure</td></tr><tr><td>TBF</td><td>Tomato*Agroecological</td><td>-</td><td>-</td><td>-</td><td>manure</td></tr></tbody></table>

compared to other faunal descriptors (e.g. richness, earthworms, ecosystem engineers), as shown in the Spearman correlation matrix (Figure S1). To account for potential confounding effects associated with differences in soil type, we included soil type as a random effect in all GLMMs. This approach allows us to isolate the net effects of macrofauna abundance and its interactions with land-use variables on soil functions, independently of variation due to soil type. While simple bivariate correlations (Figure S2) revealed significant associations with several soil properties (e.g. pH, nitrogen, aggregation), these may partly reflect underlying soil differences and were therefore further tested using hierarchical modelling. Model selection followed AICc ranking and models

with ΔAICc < 2 were considered statistically equivalent (Burnham et al., 2011). The DHARMA package was again used to assess model fit, and model averaging (Arnold, 2010) was applied to estimate coefficients (β) and their 95 % confidence intervals. Parameters with confidence intervals not overlapping zero were considered significant. All statistical analyses were performed using R software (version 4.3.3, 2023).

<caption>**Table 2**</caption>

Model-averaged standardized estimates (± SE) and marginal (R²m) and conditional (R²c) coefficients of determination for the effects of crop type (CROP), agricultural intensification (INTENS), and their interaction (CROP × INTENS) on soil fauna communities and edaphic properties. Estimates are provided for macrofauna (total abundance, ecosystem engineers, taxonomic richness, Oligochaeta, Formicidae, Isoptera, Shannon diversity), mesofauna (total microarthropod abundance, taxonomic richness, Collembola, Entomobryidae, Acari, Astigmata, Shannon diversity), and soil physicochemical and structural variables (pH-KCl, pH-H₂O, nitrogen, carbon, biogenic aggregates [BA], non-morphological aggregates [NMA], and physical aggregates [PA]). Asterisks (*) indicate significant effects at the 95 % confidence level (i.e., confidence interval excluding zero).

<table><thead><tr><th>Response variable</th><th>CROPYam ± SE</th><th>CROPTomato ± SE</th><th>INTENSIntensive ± SE</th><th>CROPYam×INTENS ± SE</th><th>CROPTomato×INTENS ± SE</th><th>R<sup>2</sup>m</th><th>R<sup>2</sup>c</th></tr></thead><tbody><tr><td>Macrofauna total abundance</td><td>-0.24 ± 0.31</td><td>-0.89 ± 0.30*</td><td>-0.66 ± 0.30*</td><td>-1.22 ± 0.50*</td><td>0.07 ± 0.46</td><td>0.506</td><td>0.506</td></tr><tr><td>Ecosystem engineers total abundance</td><td>-0.36 ± 0.38</td><td>-1.20 ± 0.38*</td><td>-0.69 ± 0.38</td><td>-1.55 ± 0.54*</td><td>-0.23 ± 0.54</td><td>0.548</td><td>0.548</td></tr><tr><td>Macrofauna taxonomic richness</td><td>-0.24 ± 0.11*</td><td>0.01 ± 0.10</td><td>-0.24 ± 0.09*</td><td>—</td><td>—</td><td>0.146</td><td>0.146</td></tr><tr><td>Oligochaeta total abundance</td><td>-1.74 ± 0.39*</td><td>-2.28 ± 0.33*</td><td>0.01 ± 0.35</td><td>-1.57 ± 0.62*</td><td>-0.18 ± 0.55</td><td>0.487</td><td>0.794</td></tr><tr><td>Formicidae total abundance</td><td>-0.35 ± 0.47</td><td>-0.84 ± 0.41*</td><td>-1.31 ± 0.39*</td><td>-1.13 ± 0.69</td><td>0.11 ± 0.68</td><td>0.417</td><td>0.417</td></tr><tr><td>Isoptera total abundance</td><td>-2.25 ± 1.49</td><td>-25.42 ± 19193.98</td><td>-26.15 ± 26870.46</td><td>—</td><td>—</td><td>0.957</td><td>0.992</td></tr><tr><td>Shannon diversity (Macrofauna)</td><td>-0.11 ± 0.13</td><td>0.30 ± 0.13*</td><td>0.07 ± 0.10</td><td>—</td><td>—</td><td>0.113</td><td>0.113</td></tr><tr><td>Microarthropods total abundance</td><td>—</td><td>—</td><td>-0.22 ± 0.23</td><td>—</td><td>—</td><td>0.016</td><td>0.020</td></tr><tr><td>Microarthropods taxonomic richness</td><td>—</td><td>—</td><td>-0.20 ± 0.08*</td><td>—</td><td>—</td><td>0.069</td><td>0.069</td></tr><tr><td>Collembola total abundance</td><td>1.56 ± 0.60*</td><td>0.72 ± 0.61</td><td>0.04 ± 0.62</td><td>-2.12 ± 0.87*</td><td>2.07 ± 0.86*</td><td>0.501</td><td>0.501</td></tr><tr><td>Entomobryidae total abundance</td><td>1.26 ± 0.50*</td><td>1.49 ± 0.52*</td><td>-1.53 ± 0.42*</td><td>—</td><td>—</td><td>0.415</td><td>0.415</td></tr><tr><td>Acari total abundance</td><td>0.05 ± 0.27</td><td>-0.44 ± 0.27</td><td>-0.41 ± 0.22</td><td>—</td><td>—</td><td>0.053</td><td>0.053</td></tr><tr><td>Astigmata total abundance</td><td>-3.64 ± 1.10*</td><td>-2.39 ± 0.92*</td><td>-0.28 ± 0.81</td><td>—</td><td>—</td><td>0.481</td><td>0.481</td></tr><tr><td>Shannon diversity (Mesofauna)</td><td>0.26 ± 0.52</td><td>-0.78 ± 0.61</td><td>-0.27 ± 0.57</td><td>0.12 ± 0.93</td><td>-1.58 ± 0.93</td><td>0.076</td><td>0.101</td></tr><tr><td>Potassium chloride pH (pH-KCl)</td><td>-0.05 ± 0.04</td><td>0.12 ± 0.04*</td><td>-0.05 ± 0.04</td><td>0.16 ± 0.05*</td><td>-0.11 ± 0.05*</td><td>0.194</td><td>0.580</td></tr><tr><td>Water pH (pH-W)</td><td>-0.05 ± 0.04</td><td>0.12 ± 0.04*</td><td>-0.05 ± 0.04</td><td>0.16 ± 0.05*</td><td>-0.11 ± 0.05</td><td>0.154</td><td>0.597</td></tr><tr><td>Nitrogen (N)</td><td>-0.28 ± 0.09*</td><td>-0.62 ± 0.09*</td><td>-0.33 ± 0.07*</td><td>—</td><td>—</td><td>0.412</td><td>0.543</td></tr><tr><td>Carbon (C)</td><td>-0.49 ± 0.10*</td><td>-0.45 ± 0.10*</td><td>-0.24 ± 0.10*</td><td>0.33 ± 0.15*</td><td>-0.16 ± 0.15</td><td>0.351</td><td>0.598</td></tr><tr><td>Biogenic aggregates (BA)</td><td>-0.75 ± 0.09*</td><td>-0.31 ± 0.09*</td><td>-0.64 ± 0.07*</td><td>—</td><td>—</td><td>0.631</td><td>0.631</td></tr><tr><td>Non-morphological aggregates (NMA)</td><td>0.68 ± 0.07*</td><td>0.51 ± 0.07*</td><td>0.33 ± 0.06*</td><td>—</td><td>—</td><td>0.575</td><td>0.649</td></tr><tr><td>Physical aggregates (PA)</td><td>0.32 ± 0.11*</td><td>-0.04 ± 0.12</td><td>0.39 ± 0.10*</td><td>-0.70 ± 0.15*</td><td>-0.14 ± 0.15</td><td>0.234</td><td>0.321</td></tr></tbody></table>
# 3. Results

## 3.1. General trends

The soil fauna abundance and taxonomic richness exhibited significant variation across cropping systems, indicative of disparities in management practices. In agroecological plots, an average of 1309 ± 195 ind.m⁻² for macrofauna was documented, constituting a mean of 7 taxonomic groups (range: 2–13). The abundance of microarthropods averaged 23226 ± 4492 ind.m⁻² and exhibited a mean of 7 taxonomic groups (range: 1–14). Conversely, intensive systems demonstrated diminished macrofauna abundance, exhibiting an average of 516 ± 88 ind.m⁻² and a mean of 6 taxonomic groups (range: 1–13). Microarthropod abundance and taxonomic richness were slightly reduced, with a mean of 18673 ± 6964 ind.m⁻² and 6 taxonomic groups (range: 0–14).

## 3.2. Soil macrofauna

The total abundance of soil macroinvertebrates was significantly higher in pasture systems compared to tomato systems (β = -0.89, P = 0.003; Table 2, Fig. 3). Agricultural intensification had a negative effect on soil macrofauna, with abundance decreasing with increasing intensification (β = -0.66, P = 0.03; Table 2, Fig. 3). A significant interaction between crop type and intensification showed that macro-invertebrate abundance decreased significantly in intensive yam systems (β = -1.22, P = 0.01; Table 2). Soil ecosystem engineers were significantly higher in pasture systems than in tomato systems (β = -1.20, P = 0.001; Table 2). The taxonomic richness of macrofauna was significantly lower in intensive plots than in agroecological plots (β = -0.23, CI = [-0.41, -0.06]; Table 2) and was also reduced in yam systems (β = -0.24, CI = [-0.46, -0.03]; Table 2). The total abundance of Oligochaeta was significantly higher in pasture systems compared to yam (β = -1.94, CI = [-2.5, -1.39]) and tomato systems (β = -2.33, CI = [-2.9, -1.77]; Table 2). For Formicidae, abundance was significantly higher in pasture systems compared to tomato systems (β = -0.82, CI = [-1.5, -0.13]; Table 2). None of the studied parameters had a significant effect on the abundance of Isoptera (P > 0.05).

## 3.3. Soil microarthropods

No variable significantly influenced the total abundance of soil microarthropods (P > 0.05; Table 2, Fig. 4). However, within

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Total abundance of soil macrofauna in 18 selected farmer plots in Guadeloupe according to farming practice (agroecological vs. intensive) and crop type (pasture vs. yam vs. tomato).</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Total abundance of soil microarthropods in 18 selected farmer plots in Guadeloupe according to farming practice (agroecological vs. intensive) and crop type (pasture vs. yam vs. tomato).</figcaption></figure>

Collembola, Entomobryidae abundance was significantly reduced in intensive systems compared to agroecological systems (β = -1.53. P = 0.0002; Table 2). Total Acari abundance was not significantly affected by any variable (P > 0.05). Microarthropod taxonomic richness was significantly reduced in intensive systems compared to agroecological systems (β = -0.20, P = 0.047; Table 2).

## 3.4. Soil physico-chemical properties

Soil pH-H2O ranged from 5.09 to 8.44, while pH-KCl ranged from 3.58 to 7.41. Soil pH-H2O was significantly higher in tomato systems compared to pasture (β = 0.121, P = 0.0015; Table 2). Soil pH-KCl was also significantly higher in tomato systems compared to pasture (β = 0.146, P = 0.004; Table 2). Total N was significantly reduced in intensive systems compared to agroecological systems (β = -0.329, P < 0.0001; Table 2) and was also lower in tomato and yam plantations compared to pastures (β = -0.622 and -0.284, P < 0.001; Table 2). Similarly, total C was lower in intensive plots compared to agroecological plots (β = -0.243, P = 0.019; Table 2) and significantly lower in tomato and yam plots compared to pastures (β = -0.455 and -0.490, P < 0.0001; Table 2). The percentage of soil biological aggregates was significantly reduced in tomato and yam plantations compared to pastures (β = -0.305 and -0.749, P < 0.001; Table 2, Fig. 5) and was lower

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Morphology of soil aggregates in 18 selected farmer plots in Guadeloupe according to farming practice (agroecological vs. intensive) and crop type (pasture vs. yam vs. tomato).</figcaption></figure>
in intensive systems compared to agroecological systems (β = -0.637, P < 0.0001; Table 2). Conversely, the percentage of non-macro-aggregated soil was significantly higher in yam and tomato systems compared to pasture (β = 0.514 and 0.677, P < 0.0001; Table 2) and in intensive systems compared to agro-ecological systems (β = 0.332, P < 0.0001; Table 2). The percentage of physical aggregates was higher in intensive systems compared to agroecological systems (β = 0.390, P = 0.001; Table 2) and in yam systems compared to pastures (β = 0.001, P < 0.01).

### 3.5. Covariations

At a finer scale, macroarthropod abundance and its interactions with crop type and intensification (MA × crop; MA × intensification) showed no significant effects on biogenic aggregates (BA), non-morphological aggregates (NMA), physical aggregates (PA), pH-KCl or N (P > 0.05; Table 3).

## 4. Discussion

### 4.1. Agroecological practices positively influence edaphic biodiversity

Our study showed that agroecological practices adopted by farmers have a positive and significant impact on several edaphic biodiversity parameters, namely the abundance of macrofauna, ecosystem engineers,

<caption>Table 3</caption>

Models explaining the variation in biogenic aggregates (BA), non-morphological aggregates (NMA), physical aggregates (PA), pH-KCl and N as a function of total macrofauna abundance (MA), crop type (CROP), agricultural intensification practices (INTENS) and their interactions (CROP × MA and INTENS × MA). The table includes models with ΔAICc < 3.

<table><thead><tr><th>Models</th><th>AICc</th><th>ΔAICc</th><th>Weight</th><th>logLik</th><th>d. f.</th></tr></thead><tbody><tr><td colspan="6"><strong>Percentage of soil biological aggregates</strong></td></tr><tr><td>BA ~ CROP + INTENS</td><td>684.792</td><td>0.000</td><td>0.309</td><td>-335.890</td><td>6</td></tr><tr><td>BA ~ CROP + INTENS + MA</td><td>686.610</td><td>1.818</td><td>0.125</td><td>-335.622</td><td>7</td></tr><tr><td>BA ~ CROP + INTENS + PH-KCL</td><td>686.623</td><td>1.831</td><td>0.124</td><td>-335.629</td><td>7</td></tr><tr><td>BA ~ CROP + INTENS + N</td><td>686.819</td><td>2.027</td><td>0.112</td><td>-335.726</td><td>7</td></tr><tr><td colspan="6"><strong>Percentage of non-macro-aggregated soil</strong></td></tr><tr><td>NMA ~ CROP + INTENS</td><td>652.735</td><td>0.000</td><td>0.303</td><td>-318.685</td><td>7</td></tr><tr><td>NMA ~ CROP + INTENS + N</td><td>653.815</td><td>1.080</td><td>0.177</td><td>-318.019</td><td>8</td></tr><tr><td>NMA ~ CROP + INTENS + N + PH-KCL</td><td>654.921</td><td>2.185</td><td>0.102</td><td>-318.571</td><td>8</td></tr><tr><td>NMA ~ CROP + INTENS + MA + N</td><td>666.823</td><td>0.000</td><td>0.149</td><td>-323.286</td><td>9</td></tr><tr><td colspan="6"><strong>Percentage of physical aggregates</strong></td></tr><tr><td>PA ~ CROP + INTENS</td><td>666.982</td><td>0.159</td><td>0.138</td><td>-323.366</td><td>9</td></tr><tr><td>PA ~ CROP + MA + PH-KCL + CROP*MA</td><td>667.215</td><td>0.393</td><td>0.123</td><td>-324.719</td><td>8</td></tr><tr><td>PA ~ CROP + MA + CROP*MA</td><td>667.332</td><td>0.509</td><td>0.116</td><td>-322.274</td><td>10</td></tr><tr><td>PA ~ CROP + INTENS</td><td>667.356</td><td>0.534</td><td>0.114</td><td>-322.286</td><td>10</td></tr><tr><td>PA ~ CROP + INTENS + MA + PH-KCL + CROP*MA</td><td>668.223</td><td>1.400</td><td>0.074</td><td>-321.419</td><td>11</td></tr><tr><td>PA ~ CROP + INTENS + MA + PH-KCL + CROP*MA + INTENS*MA</td><td>668.650</td><td>1.828</td><td>0.060</td><td>-322.933</td><td>10</td></tr><tr><td>PA ~ CROP + MA + N + PH-KCL + CROP*MA</td><td>669.141</td><td>2.318</td><td>0.047</td><td>-323.178</td><td>10</td></tr><tr><td>PA ~ CROP + INTENS + MA + N + CROP*MA</td><td>669.420</td><td>2.598</td><td>0.041</td><td>-324.585</td><td>9</td></tr></tbody></table>

and some groups of microarthropods. However, we did not demonstrate the link between agroecological practices, soil fauna and soil ecological properties, such as soil aggregation and chemical properties. The taxonomic richness of macrofauna and microarthropods was also positively influenced by agroecological practices. Higher total nitrogen and carbon, and more aggregates of biological origin were also found in these agroecological systems.

Macrofauna abundance and taxonomic richness were improved in agroecological cropping systems, regardless of crop type. Our results are consistent with other recent studies conducted in Guadeloupe that have shown that agroecological practices increase soil diversity and ecosystem services in different crops (Loranger-Merciris et al., 2022; 2023; Moulin-Rouyard et al., 2024). Other studies on a more global scale have also shown a positive effect of agroecological farming on soil biodiversity compared to conventional farming (Christel et al., 2021; Lavelle et al., 2022). Overall, agroecological practices improve habitat heterogeneity, which is crucial for maintaining a diverse faunal community, by providing varied food resources and shelter for many species (Altieri, 1999). Several other factors may explain these results, including the use of pesticides, herbicides, and mineral fertilizers in conventional farms.

Recent studies have shown that the use of herbicides and pesticides has strong negative effects on soil faunal communities in different environmental contexts (Gunstone et al., 2021; Beaumelle et al., 2023). In particular, in our study, glyphosate and glufosinate herbicides were used by farmers in intensive tomato and yam crops. El Jaouhari et al. (2023) showed that these herbicides caused a significant decrease in soil macroarthropod richness in farmers' fields in Martinique. Mean species richness was 21 % lower and the composition of the macroarthropod community was drastically altered when these herbicides were applied. Related to this result, we found in our study that there were significantly fewer earthworms in intensive tomato and yam crops where these herbicides were used. Such a change in community composition can alter ecosystem functioning and stability (Chapin et al., 2000). By limiting the use of these chemicals, agroecological systems create an environment that is more favorable for soil biota. Moreover, the absence or reduction of pesticides increases the biodiversity of soil organisms (Geiger et al., 2010). Furthermore, Beaumelle et al. (2023) pointed out that the combination of pesticides and herbicides is even more detrimental to soil fauna. In our study, the strong negative impact of tomato cultivation on soil fauna may be partly due to the combination of pesticides and herbicides during the crop cycle. The excessive use of synthetic fertilizers has also been shown to have a detrimental effect on soil communities by simplifying their composition (Beaumelle et al., 2023). However, the type and frequency of herbicide, pesticide and fertilizer applications varied between sites. These differences may have specific effects on the soil fauna that were not directly included in our analyses, but should be considered as potential confounding factors in future studies.

As expected, the impact of cultural systems on microarthropods abundance is not significant probably due to the fact that they have shorter generation times and require smaller microhabitats (Birkhofer et al., 2017). However, our results demonstrated that microarthropods taxonomic richness was significantly higher in agroecological cropping systems compared to intensive ones. Soil microarthropod richness can be stimulated by the presence of organic manure, which can create favorable environmental conditions for the growth of fungi, which are a food source for microarthropods, especially Collembola (Scheu and Simmerling, 2004; Miller et al., 2020). Regarding Collembola, Entomobyidae abundance significantly decrease in intensive systems. Other studies showed that more ecofriendly practices impacted Collembola assemblages (Coulibaly et al., 2017; Chassain et al., 2023). In particular, Chassain et al. (2023) showed that high pesticide treatment intensities strongly affected species with surface-adapted traits (as Entomobyidae).
## 4.2. Crops affect soil biological parameters

Another important result is that crops affected biological parameters, i.e. the abundance of macroinvertebrates and engineers (in particular earthworms) and some microarthropods. The soil fauna is generally less disturbed in pastures, although it remains dependent on the degree of intensification. These perennial crops are likely to provide a more stable soil structure, which supports biological activity. Earthworms, which are particularly fond of organic matter (cow dung), tend to develop in abundance. We also found the higher number of biogenic aggregates in pasture. A previous study has shown that biogenic aggregates participate in the soil structural stability and allow better carbon sequestration than physical aggregates (Carvalho da Silva Neto et al., 2016). Through improvement of soil structural stability, soil ecosystem engineers can also play a role in infiltration and distribution of water in the soil, which can impact primary production. We also found a generally higher biodiversity in pastures than in the other crops. This observation is consistent with the higher levels of organic matter in pastures compared to other crops (Garratt et al., 2018).

Yam cultivation has a semi-perennial production cycle. This may limit disturbance and provide relatively stable conditions for soil macrofauna. The plots cultivated with agroecological methods showed higher soil fauna abundance. On the other hand, intensive yam cultivation has a more negative impact on soil biodiversity. Due to their short production cycle, tomato plots are generally unfavorable for the development of soil fauna. The more frequent soil disturbance caused bytomato cultivation practices probably explained this result. Hagner et al. (2023) also showed the effect of crop type on soil biodiversity. In their study, they showed that conventional systems had a negative effect on soil microarthropod diversity, but only on cereal farms (compared to pastures). This finding is probably related to the higher physical soil disturbance in conventionally managed cereal soils due to more frequent tillage compared to pastures (Hagner et al., 2023). Furthermore, Bardgett and Wardle (2010) demonstrated that conventional systems can reduce species diversity due to stress conditions induced by tillage and the application of chemicals. In line with Hagner et al. (2023), we can conclude that the type of crop is an important factor influencing soil biodiversity. It defines the management practice and thus the soil conditions in which soil biota live.

## 5. Conclusion

This study investigated the diversity of soil fauna and its impact on soil properties across different agricultural production cycles and levels of intensification. The results showed that the fauna was generally much more abundant and diverse in agroecological plots. Our study also highlighted the need to take into account differences between crop types when comparing soil faunal communities under agroecological and conventional intensive systems. However, in our study it is difficult to distinguish the effect of the level of intensification from the effects of crop type species or plot age.

### CRediT authorship contribution statement

**Aurélie Jean-Pierre:** Writing – review & editing, Formal analysis.
**Valérie Angeon:** Writing – review & editing, Project administration, Funding acquisition.
**Harry Ozier-Lafontaine:** Writing – review & editing, Project administration, Funding acquisition.
**LORANGER-MERCIRIS Gladys:** Writing – original draft, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

### Funding

This work was supported financially by the project "Viability and adaptive governance of tropical island agrosystems – GAIA-TROP" funding by the ANR (ANR-12-AGRO-0009).

### Declaration of Competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

The authors thank Céline Martel, Christophe Latchman, Luca Romanet (INRAE Antilles Guyane) and Fred Burner (IRD) for their technical help. The authors would also like to thank Lucienne Desfontaines (INRAE Antilles Guyane) for soil chemical analyses.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.pedobi.2025.151077.

### Data availability

Data will be made available on request.

### References

Adhikari, K., Hartemink, A.E., 2016. Linking soils to ecosystem services - a global review. Geoderma 262, 101–111. https://doi.org/10.1016/j.geoderma.2015.08.009.

Altieri, M.A., 1999. The ecological role of biodiversity in agroecosystems. Agric. Ecosyst. Environ. 74, 19–31. https://doi.org/10.1016/S0167-8809(99)00028-6.

Arnold, T.W., 2010. Uninformative parameters and model selection using akaike's information criterion. J. Wildl. Manag. 74, 1175–1178. https://doi.org/10.1111/j.1937-2817.2010.rb01236.x.

Bardgett, R.D., van der Putten, W.H., 2014. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511. https://doi.org/10.1038/nature13855.

Bardgett, R.D., Wardle, D.A., 2010. Aboveground-belowground linkages: biotic interactions, ecosystem processes, and global change. Oxford University Press.

Barton, K., Barton, M.K., 2019. MuMin: multi-model inference. R. Package Version 1, 18.

Beaumelle, L., Tison, L., Eisenhauer, N., Hines, J., Malladi, S., Pelosi, C., Thouvenot, L., Phillips, H.R.P., 2023. Pesticide effects on soil fauna communities-A meta-analysis. J. Appl. Ecol. 60, 1239–1253. https://doi.org/10.1111/1365-2664.14437.

Bengtsson, J., 2002. Disturbance and resilience in soil animal communities. Eur. J. Soil Biol. 38, 119–125. https://doi.org/10.1016/S1164-5563(02)01133-0.

Birkhofer, K., Gossner, M.M., Diekötter, T., Drees, C., Ferlian, O., Maraun, M., Scheu, S., Weisser, W.W., Wolters, V., Wurst, S., et al., 2017. Land-use type and intensity differentially filter traits in above- and below-ground arthropod communities. J. Anim. Ecol. 86, 511–520. https://doi.org/10.1111/1365-2656.12641.

Bolker, B.M., Brooks, M.E., Clark, C.J., Geange, S.W., Poulsen, J.R., Stevens, M.H.H., White, J.S.S., 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135. https://doi.org/10.1016/j.tree.2008.10.008.

Brooks, Mollie E., et al., 2017. "glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling.". R. J. 9, 378–400. https://doi.org/10.3929/ethz-b-000240890.

Brussaard, L., 2021. Biodiversity and ecosystem functioning in soil: the dark side of nature and the bright side of life. Ambio 50, 1286–1288. https://doi.org/10.1007/s1328002101507-z.

Burnham, K.P., Anderson, D.R., Huyvaert, K.P., 2011. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav. Ecol. Socio 65, 23–35. https://doi.org/10.1007/s00265-010-1029-6.

Carvalho da Silva Neto, E., Gervasio Pereira, M., Fernandes, J.C.F., De Andrade Corrêa Neto, T., 2016. Aggregate formation and soil organic matter under different vegetation types in atlantic forest from southeastern Brazil. Semin. Cienc. Agrar. 37 (6), 3927–3940. https://doi.org/10.5433/1679-0359.2016v37n6p3927.

Chapin III, F.S., Zavaleta, E.S., Eviner, V.T., Naylor, R.L., Vitousek, P.M., Reynolds, H.L., Díaz, S., 2000. Consequences of changing biodiversity. Nature 405 (6783), 234–242. https://doi.org/10.1038/35012241.

Chassain, J., Joimel, S., Vieublé-Gonod, L., 2023. Collembola taxonomic and functional diversity in conventional, organic and conservation cropping systems. Eur. J. Soil Biol. 118, 103530. https://doi.org/10.1016/j.ejsobi.2023.103530.

Christel, A., Maron, P.A., Ranjard, L., 2021. Impact of farming systems on soil ecological quality: a meta-analysis. Environ. Chem. Lett. 19, 4603–4625. https://doi.org/10.1007/s10311-021-01302-y.

Clermont-Dauphin, C., Cabidoche, Y.M., Meynard, J.M., 2004. Effects of intensive monocropping of bananas on properties of volcanic soils in the uplands of the French west indies. Soil Use Manag 20, 105–113. https://doi.org/10.1111/j.1475-2743.2004.tb0345.x.

Coulibaly, S.F.M., Coudrain, V., Hedde, M., Brunet, N., Mary, B., Recous, S., Chauvat, M., 2017. Effect of different crop management practices on soil collembola assemblages:
a 4-year follow-up. Appl. Soil Ecol. 119, 354–366. https://doi.org/10.1016/j.apsoil.2017.06.013.

Domínguez, A., Jiménez, J.J., Ortíza, C.E., Bedanoa, J.C., 2018. Soil macrofauna diversity as a key element for building sustainable agriculture in Argentine pampas. Acta Oecum 92, 102–116. https://doi.org/10.1016/j.actao.2018.08.012.

El Jaouhari, M., Damour, G., Tixier, P., Coulis, M., 2023. Glyphosate reduces the biodiversity of soil macrofauna and benefits exotic over native species in a tropical agroecosystem. Basic Appl. Ecol. 73, 18–26.

FAO, 2017. The future of food and agriculture-trends and challenges. Food and Agriculture Organization of the United Nations, Rome, p. 180.

FAO, 2019. Current status of agriculture in the Caribbean and implications for agriculture policy and strategy. 2030 - food. N°14. In: Agriculture and rural development in Latin America and the Caribbean, 28. Santiago de Chile. Food and Agriculture Organization of the United Nations, Rome.

Fragoso, C., Brown, G.G., Patrón, J.C., Blanchart, E., Lavelle, P., Pashanasi, B., Senapati, B., Kumar, T., 1997. Agricultural intensification, soil biodiversity and agroecosystem function in the tropics: the role of earthworms. Appl. Soil Ecol. 6, 17–35. https://doi.org/10.1016/S0929-1393(96)00154-0.

Garratt, M.P.D., Bommarco, R., Kleijn, D., Martin, E., Mortimer, S.R., Redlich, S., Senapathi, D., Steffan-Dewenter, I., Świtek, S., Takács, V., van Gils, S., van der Putten, W.H., Potts, S.G., 2018. Enhancing soil organic matter as a route to the ecological intensification of european arable systems. Ecosyst 21, 1404–1415. https://doi.org/10.1007/s10021-018-0228-2.

Geiger, F., Bengtsson, J., Berendse, F., Weisser, W.W., Emmerson, M., Morales, M.B., Ceryngier, P., Liira, J., Tscharntke, T., Winqvist, C., Eggers, S., Bommarco, R., Pärt, T., Bretagnolle, V., Plantegenest, M., Clement, L.W., Dennis, C., Palmer, C., Onate, J.J., Inchausti, P., 2010. Persistent negative effects of pesticides on biodiversity and biological control potential on european farmland. Basic Appl. Ecol. 11, 97–105. https://doi.org/10.1016/j.baae.2009.12.001.

Gunstone, T., Cornelisse, T., Klein, K., Dubey, A., Donley, N., 2021. Pesticides and soil invertebrates: a hazard assessment. Front. Environ. Sci. 9. https://doi.org/10.3389/fenvs.2021.643847.

Hagner, M., Pohjanlehto, I., Nuutinen, V., Setälä, H., Velmala, S., Vesterinen, E., Pennanen, T., Lemola, R., Peltoniemi, K., 2023. Impacts of long-term organic production on soil fauna in boreal dairy and cereal farming. Appl. Soil Ecol. 189, 104944. https://doi.org/10.1016/j.apsoil.2023.104944.

Harrison, X.A., Donaldson, L., Correa-Cano, M.E., Evans, J., Fisher, D.N., Goodwin, C.E., Robinson, B.S., Hodgson, D.J., Inger, R., 2018. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6, e4794. https://doi.org/10.7717/peerj.4794.

Hartig, F., 2020. DHARMA: residual diagnostics for hierarchical (multi-level/mixed) regression models. R. Package Version 0 (2), 7.

Hu, J., Zhou, S., Tie, L., Liu, X., Liu, X., Zhao, A., Lai, J., Xiao, L., You, C., Huang, C., 2022. Effects of nitrogen addition on soil faunal abundance: a global meta-analysis. Glob. Ecol. Biogeogr. 31, 1655–1666. https://doi.org/10.1111/geb.13528.

ISO, 2011. Qualité du sol - Prélèvement des invertébrés du sol - Partie 5: Prélèvement et extraction des macro-invertébrés du sol. ISO 23611-5: 2011.

Jouquet, P., Dauber, J., Lagerlöf, J., Lavelle, P., Lepage, M., 2006. Soil invertebrates as ecosystem engineers: intended and accidental effects on soil and feedback loops. Appl. Soil Ecol. 32, 153–164. https://doi.org/10.1016/j.apsoil.2005.07.004.

Lavelle, P., Mathieu, J., Spain, A., Brown, G., Fragoso, C., Lapied, E., De Aquino, A., Barois, I., Barrios, E., Barros, M.E., Bedano, J.C., Blanchart, E., Caulfield, M., Chagueza, Y., Dai, J., Decaens, T., Dominguez, A., Dominguez, Y., Feijoo, A., Folgarait, P., Fonte, S.J., Gorosito, N., Huerta, E., Jimenez, J.J., Kelly, C., Loranger, G., Marchao, R., Marichal, R., Praxedes, C., Rodriguez, L., Rousseau, G., Rousseau, L., Sanabria, C., Suarez, J.C., Tondoh, J.E., De Valenca, A., Vanek, S.J., Vasquez, J., Velasquez, E., Webster, E., Zhang, C., 2022. Soil macroinvertebrate communities: a worldwide assessment. Glob. Ecol. Biogeogr. 31, 1261–1276. https://doi.org/10.1111/geb.13492.

Lee, K.E., Foster, R.C., 1991. Soil fauna and soil structure. Soil Res 29, 745–775. https://doi.org/10.1071/SR9910745.

Loranger-Merciris, G., Ozier-Lafontaine, H., Diman, J.L., Sierra, J., Lavelle, P., 2022. Fast improvement of macrofauna communities and soil quality in plantain crops converted to agroecological practices. Pedobiologia 93-94, 150823. https://doi.org/10.1016/j.pedobi.2022.150823.

Loranger-Merciris, G., Damour, G., Deloné-Louis Jeune, B., Ozier-Lafontaine, H., Dorel, M., Sierra, J., Diman, J.-L., Lavelle, P., 2023. Management practices and incidence of pests in plantain (musa paradisiaca AAB) crops. Consequences on the sustainability of the cropping systems. Appl. Soil Ecol. 189, 104904. https://doi.org/10.1016/j.apsoil.2023.104904.

Mazerolle, M.J., Mazerolle, M.M.J., 2017. Package 'AlCmodavg'. R. Package 281, 1–220.

Miller, J.J., Owen, M.L., Battigelli, J.P., Drury, C.F., Chanasyk, D.S., 2020. Short-term legacy effects of feedlot manure application on soil mesofauna. J. Environ. Qual. 49 (6), 1730–1737. https://doi.org/10.1002/jeq2.20152.

Moulin-Rouyard, C., Vaillant, V., Angeon, V., Diman, J.-L., Vaillant, J., Loranger-Merciris, G., 2024. Soil fauna and ecosystem services in agroecological cropping systems: focus on experimental open-field market gardens. Soil Syst. 8, 26. https://doi.org/10.3390/soilsystems8010026.

Phillips, H.R., Cameron, E.K., Eisenhauer, N., Burton, V.J., Ferlian, O., Jin, Y., Beaumelle, L., 2024. Global changes and their environmental stressors have a significant impact on soil biodiversity—A meta-analysis. Iscience 27 (9). https://doi.org/10.1016/j.isci.2024.110540.

Picq, C., Fouré, E., Frison, E.A., 1999. Bananas and food security. Les productions bananières, un enjeu économique majeur pour la sécurité alimentaire. International symposium, Douala, Cameroon, pp. 10–14. November 1998, 758 p. ISBN: 2-910810-36-4.

Scheu, S., Simmerling, F., 2004. Growth and reproduction of fungal feeding collmbella as affected by fungal species, melanin and mixed diets. Oecologia 139, 347–353. https://doi.org/10.1007/s00442-004-1513-7.

Sierra, J., Causeret, F., Diman, J.L., Publicol, M., Desfontaines, L., Cavalier, A., Chopin, P., 2015. Observed and predicted changes in soil carbon stocks under export and diversified agriculture in the Caribbean. The case study of Guadeloupe. Agric. Ecosyst. Environ. 213, 252–264. https://doi.org/10.1016/j.agee.2015.08.015.

Soumaré, M., Havard, M., Bachelier, B., 2021. Cotton in west and Central Africa: from the agricultural revolution to the agro-ecological transition. Cah. Agric. 30, 5. https://doi.org/10.1051/cagri/2020044.

Tamburini, G., Bommarco, R., Wanger, T.C., Kremen, C., van der Heijden, M.G.A., Liebman, M., Hallin, S., 2020. Agricultural diversification promotes multiple ecosystem services without compromising yield. Sci. Adv. 6 (45), eaba1715. https://doi.org/10.1126/sciadv.aba1715.

Tisdall, J.M., Oades, J.M., 1982. Organic matter and water-stable aggregates in soils. J. Soil Sci. 33, 141–163. https://doi.org/10.1111/j.1365-2389.1982.tb01755.x.

Turbe, A., Toni, A., Benito, P., Lavelle, P., Ruiz, N., Van der Putten, W.H., Labouze, E., Mudgal, S., 2010. Soil biodiversity: functions, threats and tools for policy makers. Bio Intelligence Service, IRD, and NIOO, Report for European Commission (DG Environment).

Velasquez, E., Pelosi, C., Brunet, D., Grimaldi, M., Martins, M., Rendeiro, A.C., Barrios, E., Lavelle, P., 2007. This ped is my ped: visual separation and near infrared spectra allow determination of the origins of soil macroaggregates. Pedobiologia 51, 75–87. https://doi.org/10.1016/j.pedobi.2007.01.002.

Voronova, L.D., 1968. The effect of some pesticides on the soil invertebrate fauna in the south taiga zone in the perm region (USSR). Pedobiologia 8, 507–525. https://doi.org/10.1016/S0031-4056(23)00345-1.

Zhang, H., Schrader, S., 1993. Earthworm effects on selected physical and chemical properties of soil aggregates. Biol. Fert. Soils 15, 229–234. https://doi.org/10.1007/BF00361617.

Zhu, Y., Bian, H., Ju, C., Xu, C., Zhou, Y., Zhang, H., Xu, X., 2023. Fertilization alters the abundance but not the diversity of soil fauna: a meta-analysis. Glob. Ecol. Biogeogr. 32, 482–494. https://doi.org/10.1111/geb.13641.