Interaction between soils physicochemical properties and fungal communities in different Tunisian agroecosystems

Interaction between soils physicochemical properties and fungal communities in different Tunisian agroecosystems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Interaction between soils physicochemical properties and fungal communities in different Tunisian agroecosystems Abdelhak Rhouma, Lobna Hajji-Hedfi, Djalel Oukid, Mohamed El Amine Kouadri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4332094/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Soil fungi are vital members of the soil ecosystem, performing a multitude of functions critical for ecosystem health. This study examined the relationship between soil properties and fungal communities in Tunisian agroecosystems. Soil characteristics like pH, electrical conductivity, bulk density, and nutrient content displayed significant variations across the studied crops. These variations suggest the need for tailored irrigation and fertilization practices for optimal plant growth. Fungal abundance also varied significantly, with pomegranate ( P. granatum cv. Gabsi: 14.82 x 10⁵ CFU/g of soil) harboring the most abundant community, while tomato ( S. lycopersicum cvs. Firenze and Dorra: 0.92 x 10⁵ and 0.66 x 10⁵ CFU/g of soil, respectively) exhibited the least. Similarly, Trichoderma spp. abundance followed the same pattern (2.02 x 10⁵, 0.04 x 10⁵, and 0.06 x 10⁵ CFU/g of soil, respectively). Analysis revealed that fungal abundance increased in soils with low salinity, compaction, and clay content, but increased more in environments rich in organic matter, nutrients, and well-drained sandy textures. Furthermore, a preference for a higher soil carbon-to-nitrogen ratio suggests fungi favor readily available carbon sources for growth. In Tunisian agroecosystems, soil properties significantly influenced fungal abundance and composition across crops. This highlights the need for tailored management practices to promote both plant growth and beneficial fungi, with a focus on maintaining good soil health for diverse fungal communities. Soil fungal densities Soil properties Trichoderma spp. Crops Figures Figure 1 Introduction In the intricate tapestry of terrestrial ecosystems, soil stands as the paramount habitat for myriad microorganisms essential to ecosystem function, imparting dynamism and biodiversity (Guo et al., 2020 ). Fungi, among these organisms, hold a pivotal role, in facilitating nutrient cycling, organic matter decomposition, and plant symbiosis (Bodenhausen et al., 2023 ; Ma et al., 2024 ). Therefore, understanding the factors shaping fungal communities within soil environments is paramount for elucidating biological processes and refining soil management practices (Fierer, 2017 ; Diao et al., 2021 ). At the core of this research lies the relationship between soil physicochemical properties and fungal density a nexus embodying the intricate interplay between abiotic and biotic factors governing soil microbial dynamics. Soil attributes, encompassing pH, texture, nutrient availability, and organic matter content, profoundly influence microbial communities, fungi included. The spatial distribution of fungal populations within soil intricately intertwines with these physicochemical attributes, delineating the heterogeneous nature of soil habitats and the diverse ecological niches they offer (Canini et al., 2019 ; Trivedi et al., 2020 ; Bodenhausen et al., 2023 ). Consequently, meticulous selection of soil sampling methods emerges as a pivotal determinant in unraveling the complexities of soil fungal ecology (Huang et al., 2020 ). Disparate sampling techniques may yield divergent insights into fungal community structure and abundance, underscoring the necessity of standardized protocols to ensure data comparability and robustness (Guo et al., 2023 ). Moreover, soil texture emerges as a chief driver in shaping fungal habitats and community composition (Grandy et al., 2009 ). Soil structure, particularly the physical arrangement of soil particles, dictates moisture retention, aeration, and nutrient availability, thereby delineating ecological niches suitable for different fungal taxa (Bardgett & van der Putten, 2014 ). Fine-textured soils, rich in clay content, often host distinct fungal populations compared to coarse, sandy soils, reflecting adaptations to diverse environmental conditions (Grandy et al., 2009 ; Iqbal et al., 2023 ). In this study, we delve into the intricate relationship between soil physicochemical properties and fungal density across diverse agroecosystems in Tunisian regions. Through precise soil sampling, comprehensive physical and chemical analyses, and fungal density assessments, we endeavor to unveil the underlying mechanisms steering fungal community dynamics and their implications for soil health and ecosystem resilience. Materials and methods Soil sampling methods Ten fields across four Tunisian regions (Gabes, Kairouan, Sidi Bouzid, and Gafsa) were targeted for soil sampling (refer to Table 1 for detailed characterization). At each site, a randomized zigzag transect was established, with samples collected at 2-meter intervals using a 7-cm-diameter soil auger at 10–30 cm depth. Ninety individual cores were combined to create each composite sample, with three replicates collected per species (approximately 200 g each). Twenty-seven samples in total were collected for each species and transported to the laboratory in sterile polythene bags. Table 1 Crops species and varieties along with their place of collection sampled in season from 2022 to 2023 Species Varieties Place of collection Punica granatum Gabsi Oasis of Chott Sidi Abdel Salam, Gabes Phoenix dactylifera Bouhattam Oasis of Chenini, Gabes Olea europaea Chetoui Chrarda, Kairouan Vitis vinifera Victoria Regueb, Sidi Bouzid Prunus persica Carioca Sidi Aich, Gafsa Solanum lycopersicum Saadha Firenze Dorra Sidi Ali Ben Aoun, Sidi Bouzid Solanum tuberosum Spunta Bir El Hafey, Sidi Bouzid Soil physical and chemical properties Twenty-seven air-dried soil samples for each species were sieved (2 mm) to remove debris, followed by physicochemical analysis including pH, electrical conductivity, bulk density, organic carbon/matter, total nitrogen, C/N ratio, texture, total limestone, available phosphorus, and exchangeable potassium, employing respective titration methods. Soil pH Soil pH was determined using a 1:2.5 soil:liquid ratio suspension. Five-gram soil samples were mixed with 12.5 mL distilled water and vortexed for 5–6 min. The suspension was then allowed to equilibrate for 20 min. Subsequently, the pH electrode of a Consort C1010 digital pH meter was inserted directly into the supernatant, and the pH value was recorded (Zhang et al., 2023 ). Electrical conductivity Following pH measurement, the same 1:2.5 soil extract was used for electrical conductivity (EC) determination. An Equiptronic's digital conductivity bridge (Consort C1010) measured the supernatant conductivity after calibration with a 0.01 M KCl solution, following the method of Ratshiedana et al. ( 2023 ). Bulk density Soil bulk density was determined through the cylinder method. Soil samples are collected in cylinders, trimmed to uniform height, and weighed fresh. After oven-drying, samples are re-weighed. Finally, bulk density is computed for each sample as dry weight divided by volume (Diao et al., 2021 ). The soil bulk density was calculated using the following formula (Diao et al., 2021 ): 𝜌𝑏 = 𝑀/𝑉 where 𝜌𝑏 is bulk density (g cm −3 ), 𝑀 is mass of oven-dry soil (g) and 𝑉 is the volume of soil (cm 3 ). Organic carbon and organic matter Soil organic carbon (OC) was determined by the Walkley-Black method (0.1 g soil, 10 ml 1 N K 2 Cr 2 O 7 , 20 ml H 2 SO 4 , cooling, 5 ml H 3 PO 4 , 25 ml H 2 O, diphenylamine indicator, titration with 0.5 N Fe(NH 4 ) 2 (SO 4 ) 2 endpoint). A blank control was included. Organic matter (OM) was subsequently estimated using a 1.724 conversion factor based on the assumption of 58% carbon content (OM = OC x 1.724) (Rhouma et al., 2021 ). Estimation of total nitrogen (N) and carbon and nitrogen ratio (C/N) Total soil nitrogen-supplying potential was assessed by alkaline potassium permanganate distillation, measuring liberated ammonia (hydrolyzed amino-N) as an index of nitrogen status. Available N was estimated by the Kjeldahl method (20 g soil, 0.32% KMnO 4 , 25% NaOH, distillation, boric acid/indicator trap). The pinkish color turning green upon ammonia absorption indicated completion, followed by titration with 0.02 N H 2 SO 4 to the original pink for N quantification (Kjeldahl, 1883 ). Additionally, soil C/N ratio (carbon and nitrogen ratio) was calculated according to Zhang et al. ( 2011 ). Soil texture Soil texture, defined as the relative proportions of sand (2-0.05 mm), silt (0.05 − 0.002 mm), and clay (< 0.002 mm), was determined using the Robinson pipette method following wet sieving for sand fractions. Fine material suspensions underwent sedimentation analysis with pipette extraction at predetermined depths, dried at 105°C, and quantified to classify soil texture based on the textural triangle (Jackson, 1979 ; Mathieu and Pieltain, 2003 ). Available total limestone Available total limestone in soil samples was estimated using Bernard's calcimeter by measuring CO 2 evolution upon the addition of 50% HCl. This analysis monitored the CO 2 released from a known amount of pure, dry CaCO 3 reacting with a precisely weighed soil sample, based on the principle of reaction stoichiometry (Petard, 1993 ). Available phosphorus Sodium bicarbonate-extractable phosphorus (P) was determined in 2.5 g soil samples shaken with 20 mL 2% solution and charcoal for 30 minutes. After filtration, 5 mL aliquots were reacted with a composite reagent (5 mL of 3% ammonium molybdate, 12.5 mL of sulfuric acid, 5 mL of ascorbic acid, and 25 mL of potassium antimony tartrate), and incubated for 5 min, and the resulting blue color development quantified at 660 nm using UV spectrophotometry (Bray & Kurtz, 1945 ; Olsen et al., 1954 ). Available potassium Soil available potassium was analyzed through a chemical extraction method (10 g soil, 40 mL 2% ammonium acetate, 1 hour shaking, filtration). The filtrate volume was adjusted to 40 mL, and potassium concentration was measured by flame photometry after standardization and calibration with potassium solutions (10, 15, and 40 ppm) (Van Rast et al., 1999 ). Distribution of soil fungal community and Trichoderma spp. A soil suspension (10 g soil in 90 mL sterile water) underwent serial dilutions (10 − 1 to 10 − 7 ) for fungal isolation via the dilution-plate method. Aliquots (0.1 mL) were plated onto PDA media and incubated in the dark at 25 ± 2°C for 15–21 days (Matrood et al., 2021 ). Trichoderma spp. identification relied on sequential sub-culturing for purification, followed by phenotypic examination (colony morphology, microscopic features of mycelium, conidiophores, conidia, and sexual forms) with cotton blue mounts, utilizing identification keys (Domsch et al., 1980 ; Kirk & Ansell, 1992 ). Taxonomic assignments were validated against Index Fungorum ( www.indexfungorum.org ). To estimate fungal and Trichoderma spp. populations, Mouria et al. (2012)'s formula was adopted. Plates with 30–300 colonies at consecutive dilutions were retained. Colony forming units (CFU/g soil) were calculated as the total colony count divided by 0.1 multiplied by the sum of retained plates at each dilution, then multiplied by the dilution factor. Colony forming units of the fungal population (CFU/g soil) were calculated as: (Total colonies) / (0.1 * (Σ retained plates for the first and second dilutions) * dilution factor), where Σ denotes the sum of retained plates at each dilution. Colony forming units of Trichoderma spp. (CFU/g soil) were calculated as: (Total colonies of Trichoderma spp.) / (0.1 * (Σ retained plates for the first and second dilutions) * dilution factor), where Σ denotes the sum of retained plates at each dilution. Statistical analysis The experiment employed a one-way analysis of variance (ANOVA) to assess variations among treatment groups. Data from replicates were averaged, and the resulting means were subjected to the ANOVA using SPSS version 20.0 software. Before the ANOVA, normality, and homogeneity of variance assumptions were verified using tests like Duncan's Multiple Range Test. This same test (Duncan's Multiple Range Test) was subsequently employed to identify statistically significant differences (P ≤ 0.05) between treatment means. Results Soil physical and chemical properties Table 2 summarized the physicochemical properties of various soil samples collected around different vegetable species. The analysis revealed significant differences in these properties across the crops (P < 0.01). Soil pH ranges from slightly acidic ( P. persica cv. Carioca; 6.49) to moderately alkaline ( P. dactylifera cv. Bouhattam; 8.69). Electrical conductivity, a measure of salt content, is highest in S. lycopersicum cv. Dorra (2.92 ds.m − 1 ) and lowest in P. granatum cv. Gabsi (1.30 ds.m − 1 ). Bulk density, an indicator of soil compaction, is highest in S. tuberosum cv. Spunta (2.71 g.cm − 3 ) and lowest in P. granatum cv. Gabsi (1.34 g.cm − 3 ) (Table 2 ). Table 2 Comparison of soil physicochemical properties at different vegetables species Species pH EC (ds.m − 1 ) BD (g.cm − 3 ) OC (%) OM (%) TN (%) C/N Clay (%) Silt (%) Sand (%) TL P 2 O 5 (ppm) K 2 O (ppm) Punica granatum cv. Gabsi 7.73 ± 0.18bc 1.30 ± 0.07f 1.34 ± 0.48e 0.88 ± 0.08a 1.52 ± 0.48a 0.12 ± 0.07a 7.79 ± 0.52ab 23.80 ± 1.74d 6.33 ± 0.26b 69.87 ± 1.81a 1.17 ± 0.18e 7.38 ± 0.69bc 801 ± 2.08ab Phoenix dactylifera cv. Bouhattam 8.69 ± 0.62a 1.85 ± 0.09cd 2.20 ± 0.26cd 0.36 ± 0.02de 0.62 ± 0.07de 0.05 ± 0.04c 7.06 ± 0.49ab 21.31 ± 1.08d 8.23 ± 0.79a 70.46 ± 1.48a 1.29 ± 0.17d 9.04 ± 0.88a 893.67 ± 2.47a Olea europaea cv. Chetoui 7.51 ± 0.54c 1.52 ± 0.18e 1.28 ± 0.14e 0.75 ± 0.01ab 1.30 ± 0.37ab 0.08 ± 0.01b 9.03 ± 0.97a 40.75 ± 1.29c 3.32 ± 0.49d 55.93 ± 1.91b 1.41 ± 0.09d 6.03 ± 0.74d 605.33 ± 2.52bcd Vitis vinifera cv. Victoria 7.16 ± 0.09c 1.75 ± 0.16d 1.94 ± 0.89d 0.63 ± 0.01bc 1.09 ± 0.29bc 0.08 ± 0.01b 8.27 ± 0.82ab 45.48 ± 1.72c 5.61 ± 0.72bc 48.91 ± 2.01c 1.59 ± 0.08c 6.16 ± 0.34cd 659.33 ± 1.98bc Prunus persica cv. Carioca 6.49 ± 0.89d 1.74 ± 0.03d 1.96 ± 0.07d 0.46 ± 0.03cd 0.79 ± 0.02cd 0.08 ± 0.01b 5.99 ± 0.69abc 51.64 ± 1.36b 3.32 ± 0.29d 45.04 ± 1.18cd 1.54 ± 0.26c 8.19 ± 0.62ab 704.33 ± 1.72abc Solanum lycopersicum cv. Saadha 7.79 ± 0.94bc 1.93 ± 0.11cd 2.35 ± 0.13bc 0.25 ± 0.02ef 0.43 ± 0.02ef 0.03 ± 0.01cd 7.47 ± 0.67ab 50.68 ± 1.33b 6.64 ± 0.37b 42.69 ± 1.26de 2.09 ± 0.52ab 7.03 ± 0.46bcd 554.33 ± 2.18cd S. lycopersicum cv. Firenze 7.60 ± 0.46c 2.34 ± 0.12b 2.47 ± 0.22ab 0.05 ± 0.08g 0.09 ± 0.01g 0.03 ± 0.01cd 1.58 ± 0.19c 53.92 ± 0.98ab 3.36 ± 0.82d 42.71 ± 1.76de 2.02 ± 0.37b 7.54 ± 0.94b 449 ± 1.69d S. lycopersicum cv. Dorra 7.54 ± 0.59c 2.92 ± 0.28a 2.62 ± 0.69a 0.05 ± 0.08g 0.09 ± 0.03g 0.03 ± 0.01cd 1.23 ± 0.55c 52.71 ± 1.45b 4.32 ± 0.03cd 42.96 ± 1.37de 1.98 ± 0.01b 7.53 ± 0.63b 622.33 ± 1.29bcd S. tuberosum cv. Spunta 8.27 ± 0.69ab 2.02 ± 0.06c 2.71 ± 0.72a 0.09 ± 0.07fg 0.16 ± 0.01fg 0.02 ± 0.01d 3.20 ± 0.38bc 57.99 ± 1.62a 3.28 ± 0.19d 38.73 ± 1.59e 2.18 ± 0.44a 7.04 ± 0.49bcd 766.67 ± 3.09ab P-value b < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 a Means± standard error in a column followed by the same letter are not significantly different according to Duncan’s Multiple Range Test. b Probabilities associated with individual F tests. EC: Electrical conductivity; BD: Bulk density; OC: Organic carbon; OM: Organic matter; TN: Total nitrogen; C/N: Carbon/total nitrate ratio; TL: Total limestone. An examination of soil composition across various crops revealed potential variations in irrigation needs and inherent nutrient availability. Organic carbon and organic matter content, crucial for nutrient retention, are highest in P. granatum cv. Gabsi (0.88 and 1.52%, respectively) and lowest in S. lycopersicum cv. Firenze and cv. Dorra (0.05 and 0.09%, respectively). Total nitrogen follows a similar trend. The C/N ratio, reflecting nutrient availability, is highest in O. europaea cv. Chetoui (9.03) and lowest in S. lycopersicum cv. Dorra (1.23). Clay content is highest in S. tuberosum cv. Spunta (57.99%) and lowest in P. dactylifera cv. Bouhattam (21.31%), while sand content shows the opposite trend (3.28 and 8.23%, respectively). The high sand content observed in most species suggests these soils may have good drainage properties. This could necessitate adjustments to watering regimes to prevent underwatering. Conversely, crops like tomatoes (42% sand) and potatoes (38.73% sand) with lower sand content might require more frequent, controlled irrigation to avoid waterlogging due to potentially higher water retention capacity. Furthermore, analysis of total limestone content displayed variation between crops, ranging from 1.17% in P. granatum cv. Gabsi to 2.18% in S. tuberosum cv. Spunta. Finally, the analysis of mineral nutrients, specifically P 2 O5 and K 2 O, revealed significant differences between the crops. P. dactylifera cv. Bouhattam demonstrated the highest levels of both P 2 O 5 (9.04 ppm) and K 2 O (893.67 ppm), potentially indicating a naturally richer soil environment for this particular cultivar. On the other hand, O. europaea cv. Chetoui (P 2 O 5 ; 6.03 ppm) and S. lycopersicum cv. Firenze (K 2 O; 449 ppm) exhibited the lowest levels of these nutrients, suggesting a potential need for additional fertilization for optimal growth. This data highlights the importance of considering individual crop characteristics and soil composition when developing targeted irrigation and nutrient management strategies (Table 2 ). Distribution of soil fungal community Table 3 revealed significant variations in the abundance of fungal communities across the soil samples collected around different vegetable species. There's a statistically significant difference between fungal abundance in all the compared crops (P < 0.01). Fungal colony forming units (CFU) per gram of soil serve as a proxy for fungal abundance. P. granatum cv. Gabsi exhibited the most abundant fungal community (14.82 x 10 5 CFU/g), while S. lycopersicum (cvs. Firenze and Dorra) have the least (0.92 x 10 5 and 0.66 x 10 5 CFU/g, respectively) (Table 3 ). Table 3 Distribution of soil fungal community Species Fungal community (10 5 CFU/g of soil) Punica granatum cv. Gabsi 14.82 ± 1.28a a Phoenix dactylifera cv. Bouhattam 5.62 ± 1.08e Olea europaea cv. Chetoui 9.37 ± 0.91b Vitis vinifera cv. Victoria 7.69 ± 0.87d Prunus persica cv. Carioca 7.85 ± 0.67c Solanum lycopersicum cv. Saadha 4.52 ± 0.55f S. lycopersicum cv. Firenze 0.92 ± 0.01h S. lycopersicum cv. Dorra 0.66 ± 0.16i S. tuberosum cv. Spunta 2.93 ± 0.48g P-value b < 0.01 a Means± standard error in a column followed by the same letter are not significantly different according to Duncan’s Multiple Range Test. b Probabilities associated with individual F tests. Distribution of Trichoderma spp. Table 4 highlighted a significant variation in the abundance of Trichoderma spp. across the investigated vegetable crops (P < 0.01). P. granatum cv. Gabsi displays the highest abundance (2.02 x 10 5 CFU/g), whereas S. lycopersicum (cvs. Firenze and Dorra) have the lowest (0.04 x 10 5 and 0.06 x 10 5 CFU/g, respectively) (Table 4 ). Table 4 Distribution of Trichoderma spp. Species Trichoderma spp. (10 5 CFU/g of soil) Punica granatum cv. Gabsi 2.02 ± 0.05a a Phoenix dactylifera cv. Bouhattam 0.80 ± 0.04c Olea europaea cv. Chetoui 0.99 ± 0.01b Vitis vinifera cv. Victoria 0.66 ± 0.01d Prunus persica cv. Carioca 0.70 ± 0.02d Solanum lycopersicum cv. Saadha 0.21 ± 0.03e S. lycopersicum cv. Firenze 0.06 ± 0.01f S. lycopersicum cv. Dorra 0.04 ± 0.01f S. tuberosum cv. Spunta 0.07 ± 0.01f P-value b < 0.01 a Means± standard error in a column followed by the same letter are not significantly different according to Duncan’s Multiple Range Test. b Probabilities associated with individual F tests. Factors associated with soil fungal community in soil Table 5 delved into the intricate connections between soil physicochemical properties and the distribution of soil fungal communities. Fungal abundance (CFU) exhibited a strong negative correlation with electrical conductivity (r = -0.883), bulk density (r = -0.895), clay content (r = -0.652), and total limestone (r = -0.821). This implies that fungal communities are less prevalent in soils with high levels of dissolved salts, compaction, and clay textures. On the other hand, Table 5 highlighted a robust positive correlation between fungal CFU and organic carbon (r = 0.941), organic matter (r = 0.941), total nitrogen (r = 0.935), and sand content (r = 0.680). This indicates that fungal communities increased in soil environments enriched with organic carbon and matter, characterized by fertile and well-draining soil with a good balance of nutrients. Additionally, the significant positive correlation between the C/N ratio (r = 0.686) and fungal abundance implies that fungal communities prefer soils with a higher ratio of organic carbon to total nitrogen, potentially due to a readily available carbon source for their growth (Table 5 ). Table 5 Correlation coefficients between soil physicochemical properties and distribution of soil fungal community pH EC BD OC OM TN C/N Clay Silt Sand TL P 2 O 5 K 2 O CFU − .184 − .883 ** − .895 ** .941 ** .941 ** .935 ** .686 ** − .652 ** .256 .680 ** − .821 ** − .157 .398 * pH .062 .276 − .226 − .226 − .327 − .059 − .364 .446 * .332 .060 .197 .342 EC .782 ** − .840 ** − .840 ** − .746 ** − .746 ** .555 ** − .224 − .579 ** .691 ** .141 − .347 BD − .927 ** − .927 ** − .891 ** − .661 ** .579 ** − .100 − .625 ** .811 ** .282 − .123 OC 1.000 ** .896 ** .815 ** − .620 ** .212 .652 ** − .828 ** − .289 .267 OM .896 ** .815 ** − .620 ** .212 .652 ** − .828 ** − .289 .267 TN .530 ** − .583 ** .123 .626 ** − .831 ** − .168 .324 C/N − .510 ** .380 .504 ** − .605 ** − .240 .200 Clay − .713 ** − .994 ** .856 ** − .339 − .564 ** Silt .632 ** − .386 * .356 .408 * Sand − .886 ** .319 .560 ** TL − .165 − .512 ** P 2 O 5 .338 a Means± standard error in a column followed by the same letter are not significantly different according to Duncan’s Multiple Range Test. b Probabilities associated with individual F tests. EC: Electrical conductivity; BD: Bulk density; OC: Organic carbon; OM: Organic matter; TN: Total nitrogen; C/N: Carbon/total nitrate ratio; TL: Total limestone. Principal component analysis (PCA) revealed a significant influence of soil physicochemical properties, soil fungal community composition, and Trichoderma spp. density on the observed effects. The first two principal components (PC1 and PC2) explained a combined variance of 83.237% (Fig. 1 ). PC1 contributed the most, accounting for 63.123% of the total variance. This component exhibited a positive correlation with the soil fungal community structure, Trichoderma spp. density, total nitrogen, organic carbon, organic matter content, and the C/N ratio. Conversely, PC2, explaining 20.114% of the variance, showed positive correlations with silt content, potassium (K₂O), phosphorus (P₂O₅), and soil pH (Fig. 1 ). Discussion Soil represents a complex system arising from interactions between a highly variable physicochemical matrix and diverse assemblages of organisms and nutrients. Natural processes can disrupt this intricate balance, leading to the degradation of soil properties and its ability to support biomass production. Effective management of soil nutrients hinges on understanding the soil-microbial community-plant system. This can be achieved by quantifying the soil proprieties and microbial densities, allowing for the establishment of a balanced relationship between these components for optimal ecosystem function (Rhouma et al., 2019 ; Rhouma et al., 2021 ; Iqbal et al., 2023 ). The study likely investigated the relationship between different vegetable crops and the fungal communities in the soil, particularly Trichoderma spp. The findings suggest that the composition and abundance of fungi in the soil varied depending on the vegetable crop planted. Interestingly, the pomegranate cultivar Gabsi was observed to have the highest abundance of fungi, while tomato varieties Firenze and Dorra showed the least. This indicates that different vegetable crops might influence the type and amount of fungi present in the surrounding soil. In simpler terms, the type of vegetables you plant might affect the types of fungi living in the soil around them (Rhouma et al., 2019 ; Rhouma et al., 2021 ; Janowski & Leski, 2022 ). Plant communities play a key role in shaping the composition and diversity of soil fungal communities through both direct and indirect interactions (Burke et al., 2009 ; Rousk and Bååth, 2011 ; Tedersoo et al., 2020 ). Plants directly interact with specific fungal groups, such as mycorrhizal and pathogenic fungi. Additionally, they indirectly modify the local soil environment by releasing carbohydrates through their roots (Moll et al., 2016 ; Janowski & Leski, 2022 ). Plant litter deposition also influences soil acidity and nutrient content (Aponte et al., 2010 ), while dead wood and organic matter contribute further organic material (Mäkipää et al., 2017 ). The varying degrees of plant interaction among different soil fungal trophic guilds leads to differential distribution patterns within the soil community (Tedersoo et al., 2020 ). Furthermore, the leaf litter can decrease local soil pH, hindering the activity of many fungal taxa (Tedersoo et al., 2020 ; Janowski & Leski, 2022 ). Plant-microbe interactions and niche adaptations significantly influence fungal community composition across the rhizosphere. Plant root exudates containing signaling molecules (flavonoids, strigolactones) and various organic compounds (organic acids, amino acids, proteins, fatty acids) shape these interactions (Sundin & Jacobs, 1999 ; Zhalnina et al., 2018 ). Additionally, factors like temperature, oxygen levels, and UV light play a role, and these factors vary considerably across locations, plant root structures, and plant types themselves (Põlme et al., 2018 ; Chen et al., 2020 ; Zhang et al., 2020 ). The rhizoplane, the root surface zone directly influenced by root exudates, exhibits lower fungal diversity and richness compared to the surrounding rhizosphere soil. This suggests selective pressure from plant roots limits the number of colonizing species (Lee et al., 2019 ). Consequently, the fungal communities within each rhizo-compartment (rhizoplane vs. rhizosphere) are distinct and susceptible to further manipulation by host-controlled processes (Gottel et al., 2011 ; Janowski & Leski, 2022 ). Potential variations in communication and mutualistic relationships between plants and fungi might also exist between these compartments. For example, mycorrhizal fungi interacting with root cortical cells may influence the colonization of other fungal species within the rhizosphere (Parniske, 2008 ; Iqbal et al., 2023 ). The interplay of plant immunity and microbial competition likely exerts different selective pressures on the root surface compared to the bulk soil. This phenomenon could ultimately lead to the development of unique fungal communities adapted specifically to the rhizoplane environment (Parniske, 2008 ; Zamioudis et al., 2014 ; Stringlis et al., 2018 ; Iqbal et al., 2023 ). Despite their crucial role as plant symbionts, fungal endophytes ( Trichoderma spp.) remain a relatively unexplored component of the soil fungal community. Due to their recent rise in scientific interest, limited research exists on the factors shaping their distribution patterns. While most fungal endophytes exhibit generalist tendencies, colonizing a wide range of plant taxa (Suryanarayanan et al., 2018 ; Janowski & Leski, 2022 ). U’Ren et al. ( 2019 ) suggested that plant host identity at the clade level might influence their soil distribution. Our findings demonstrate a significant influence of tomato variety on the associated fungal community. Our finding is in concordance with previous studies in maize ( Zea mays ) where variety did not significantly impact alpha diversity (species richness within a site) but did influence beta diversity (species composition differences between sites) to some extent (Kong et al., 2020 ). Similar to our results, potato ( Solanum tuberosum ) variety has been shown to significantly affect fungal community composition (Hannula et al., 2010 ; Kong et al., 2020 ; Guo et al., 2023 ). Plant root secretions provide carbon substrates, including primary and secondary metabolites, utilized by fungi (Jones et al., 2004 ; Broeckling et al., 2008 ; Guo et al., 2023 ). This suggests that different plant varieties may influence the resident soil fungal community through variations in their root exudate composition. This aligns with prior research demonstrating the ability of diverse plant species to shape fungal communities via root secretions. The observed differences in fungal communities associated with different tomato varieties potentially stem from this mechanism (Jones et al., 2004 ; Broeckling et al., 2008 ; Guo et al., 2023 ). Studies have shown a strong correlation between changes in soil environmental factors and soil microbial biomass (Zhao et al., 2009 ). Particularly, research suggests that improvements in soil porosity and moisture content are key drivers of increased microbial biomass and activity (Hu et al., 2016 ). Enhanced porosity is believed to improve soil aeration, creating a more optimal environment for soil microorganisms by facilitating gas exchange and potentially mitigating limitations caused by low oxygen availability (Zhao et al., 2022 ). Soil compaction, often caused by heavy machinery or unsustainable cropping practices, negatively impacts soil physical properties, potentially hindering microbial activity and essential biochemical processes crucial for nutrient availability. Research demonstrates a linear decline in microbial populations with increasing soil bulk density (Li et al., 2002 ). Li et al. ( 2002 ) observed a 26–39% decrease in microbial communities when soil bulk density increased from 1.00 to 1.60 mg m-3. These findings align with studies by Landina and Klevenskaya ( 1985 ) and Smeltzer et al. ( 1986 ) showing a reduction in microbial biomass carbon with compaction, which strongly correlates with microbial numbers. While plate counting methods used only capture a fraction of the total soil microbial community, the significant decrease in measured populations underscores the detrimental effect of soil compaction on microbial activity (Torsvik et al., 1996 ). Soil texture, defined by the relative proportions of sand, silt, and clay, is a well-established factor influencing soil microbial communities (Fierer, 2017 ). Finer textured soils, with higher clay content, tend to have a greater surface area and water-holding capacity (Mathieu & Pieltain, 2003 ; Lucas et al., 2014 ). This creates more favorable conditions for a wider variety and abundance of microbes. Conversely, coarse-textured soils with high sand content offer less surface area and limited water retention, potentially limiting microbial diversity and favoring microbes adapted to drier environments. Ultimately, soil texture influences the physical environment that soil microbes inhabit, impacting factors like oxygen availability, nutrient absorption, and water accessibility, all of which play a crucial role in determining the types and overall activity of the soil microbial community (Grandy et al., 2009 ; Lucas et al., 2014 ; Diao et al., 2021 ). Soil pH acts as a key environmental factor influencing the composition and function of microbial communities. It can directly impact microbial activity by altering the efficiency and functionality of enzymes critical for various metabolic processes. Additionally, pH indirectly affects microbial communities by influencing the solubility and availability of essential nutrients such as phosphorus and potassium. This altered nutrient profile can then selectively favor specific microbial taxa with adaptations to grow under those conditions. Studies by Bodenhausen et al. ( 2023 ) support this notion, demonstrating a significant influence of potassium and phosphorus on fungal community structure in arable soils. Similarly, research by Fierer & Jackson ( 2006 ) has established the well-known role of nitrogen and phosphorus availability in shaping the composition of microbial communities. Soil characteristics including pH, nitrate nitrogen content, organic carbon level, and clay content have been identified as key factors shaping fungal community composition (Huang et al., 2020 ). This aligns with previous research highlighting the prominent roles of pH and organic carbon in structuring overall soil fungal communities (Grandy et al., 2009 ; Ma et al., 2019 ). Organic matter amendments, by optimizing soil structure, demonstrably promote fungal abundance (Lucas et al., 2014 ; Huang et al., 2020 ). Additionally, factors like soil salinity and land-use type exert significant influence on the composition and structure of fungal communities (Rath et al., 2019 ). Land-use type or past land management practices have also been shown to play a part in shaping fungal community structure (Ma et al., 2019 ). This collective evidence underscores the multifaceted nature of factors influencing fungal communities in soil ecosystems (Huang et al., 2020 ). Aciego Pietri et al. (2008) and Deng et al. (2017) observed an inverse relationship between soil pH, total nitrogen content, and the composition of microbial communities, which aligns with previous research. This suggests that lower pH and higher total nitrogen levels influence microbial community structure (Li et al., 2023 ). organic matter emerges as a key factor shaping fungal communities, as evidenced by its positive correlation with numerous fungal species (Wei et al., 2022 ). Furthermore, findings by Sun et al. ( 2021 ) on total nitrogen, organic carbon, and organic matter mirrored the present study's observations. Network analysis revealed positive correlations between total nitrogen and 30 fungal species, while organic matter showed a similar positive association with 27 species, many of which overlapped with those linked to total nitrogen. In contrast, available phosphorus only correlated positively with 12 fungal species (Billah et al., 2019 ). These findings, along with those of Ma et al. ( 2024 ), suggest that environmental factors like organic matter, total nitrogen, and available phosphorus promote the growth and reproduction of diverse fungal communities, whereas pH acts as a potential inhibitor for many microbial taxa. Notably, variations in nitrogen, phosphorus, and potassium availability are crucial for microbial development and reproduction (Billah et al., 2019 ). In conclusion, the interplay between environmental factors and microbial communities is complex. These factors can directly and indirectly influence the composition and development of microbial communities, impacting nutrient cycling and overall soil quality (Ma et al., 2024 ). Our study confirms previous findings (Cline et al., 2018 ; Canini et al., 2019 ; Guo et al., 2020 ) that both soil properties and plant communities are key factors shaping fungal community composition in the soil. Soil properties directly influence fungal communities by providing essential nutrients, with variations in these properties leading to shifts in fungal composition (Yu et al., 2019 ; Zhang et al., 2020 ). Plant communities likely play a similar role due to their complex interactions with soil fungi. For instance, plant pathogens rely heavily on specific plant hosts (Nanjundappa et al., 2019 ). Furthermore, interactions occur among the different fungal groups themselves. Symbiotic fungi and plant pathogens often exhibit mutual inhibition (Li et al., 2021 ), ultimately influencing the overall fungal community composition. Consequently, both soil properties and plant communities significantly impact not just the total fungal composition but also the functional roles played by different fungal ecological groups within the soil (Rodriguez-Ramos et al., 2021 ). Given these intricate interactions, further research is warranted to explore the potential triangular relationship between plant communities, soil fungal diversity, and abiotic factors. Conclusions This study revealed a strong link between soil health and fungal communities in Tunisian agroecosystems. Variations in soil properties, including pH, salinity, compaction, organic matter, and nutrients, significantly impacted fungal abundance and composition across different crops. This emphasizes the need for tailored irrigation and fertilization practices to optimize both plant growth and potentially beneficial fungal communities. The observed preference of fungi for well-aerated, fertile soils with readily available carbon underscores the importance of maintaining good soil health for fostering diverse and abundant fungal populations. Future research could explore how agricultural practices can be further optimized to promote these beneficial soil-fungal relationships for enhanced ecosystem health and agricultural productivity. Declarations Acknowledgements The authors are grateful to the review editor and the anonymous reviewers for their helpful comments and suggestions to improve the clarity of the research paper. Funding This research was funded by the REVINE (Regenerative agricultural approaches to improve ecosystem services in Mediterranean vineyards) project, affiliated with the PRIMA program Data availability All relevant data are within the paper. Ethics declarations Human participants and/or animals This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent Informed consent was obtained from all individual participants included in the study. All authors have approved the manuscript for submission. Ethical approval Not applicable. Consent to participate Not applicable. Consent for publication Not applicable. Consent to Publish Not applicable. Competing interests The authors declare that they have no conflict of interest. References Aciego Pietri, J. C., & Brookes, P. C. (2008). Relationships between soil pH and microbial properties in a UK arable soil. Soil Biology & Biochemistry , 40 , 1856–1861. https://doi.org/10.1016/j.soilbio.2008.03.020 . Aponte, C., García, L. V., Marañón, T., & Gardes, M. (2010). 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4332094","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299611441,"identity":"f0f1e4f2-aea6-4e6a-bb57-210989dbe9f4","order_by":0,"name":"Abdelhak Rhouma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYDACCQYDhocNQIKBB8yXAxEHHhDSkoikxRisJYEULUA2EODTwj+7eeODxB12xgzSZw8+5s2xS58fdvgh0BY7Od0GHJbcOVZskHgm2YyBLy/ZmHdbcu7G22kGQC3JxmYHcFhzI8dMIrGN2YaBh8dMmncbc+7G2QkgLQcSt+HQIn8jx/xHYls9TEt9uuHs9A94tRgAbWFIbDtsBtVyOEFeOge/LYZAvwAddtyYjYfH2HDutuOGG6RzCg4kGOD2i9zt5o0fPrZVG/bz8Bg+eLutWl5+dvrmDx8q7ORweh8G2OBOBas0IKAcBcg3kKJ6FIyCUTAKRgIAAHz7XcCA+0WFAAAAAElFTkSuQmCC","orcid":"","institution":"CRRA: Centre Regional des Recherches Agricoles de Sidi Bouzid","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Abdelhak","middleName":"","lastName":"Rhouma","suffix":""},{"id":299611442,"identity":"2cd6bf3c-770c-434f-ba9b-71a399c32ae9","order_by":1,"name":"Lobna Hajji-Hedfi","email":"","orcid":"","institution":"CRRA: Centre Regional des Recherches Agricoles de Sidi Bouzid","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lobna","middleName":"","lastName":"Hajji-Hedfi","suffix":""},{"id":299611443,"identity":"8d254f77-fe2b-4e23-b9bf-32fec8c1ee7a","order_by":2,"name":"Djalel Oukid","email":"","orcid":"","institution":"Universite Mustapha Stambouli Mascara","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Djalel","middleName":"","lastName":"Oukid","suffix":""},{"id":299611444,"identity":"c956bc05-5f25-45b0-a296-1bcf64d8c065","order_by":3,"name":"Mohamed El Amine Kouadri","email":"","orcid":"","institution":"Universite Mustapha Stambouli Mascara","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"El Amine","lastName":"Kouadri","suffix":""}],"badges":[],"createdAt":"2024-04-27 03:05:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4332094/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4332094/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56446261,"identity":"ba7d84e8-5c67-45ba-9d8d-2c01a0795cb7","added_by":"auto","created_at":"2024-05-14 09:39:17","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":266096,"visible":true,"origin":"","legend":"\u003cp\u003ePrinciple component analysis (PCA) of the first two principal components, depicting relationship among soil physicochemical properties (EC: Electrical conductivity; BD: Bulk density; OC: Organic carbon; OM: Organic matter; TN: Total nitrogen; C/N: Carbon/total nitrate ratio; TL: Total limestone; P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e: available phosphorus; K\u003csub\u003e2\u003c/sub\u003eO: exchangeable potassium; sand; clay; and silt), soil fungal community (CFU_Fg), and \u003cem\u003eTrichoderma\u003c/em\u003e spp. (CFU_Tricho) density. The first (PC1) and second (PC2) principal components explain 83.237% of the variance\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4332094/v1/93a49cfa8e7c77c465855eab.jpeg"},{"id":59343109,"identity":"bbbe5a36-fc3f-4d98-bd82-fc50c0e9a2c0","added_by":"auto","created_at":"2024-06-30 06:59:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1149920,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4332094/v1/3a104a21-408f-4d6b-97b7-726c34b04f16.pdf"}],"financialInterests":"","formattedTitle":"Interaction between soils physicochemical properties and fungal communities in different Tunisian agroecosystems","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the intricate tapestry of terrestrial ecosystems, soil stands as the paramount habitat for myriad microorganisms essential to ecosystem function, imparting dynamism and biodiversity (Guo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Fungi, among these organisms, hold a pivotal role, in facilitating nutrient cycling, organic matter decomposition, and plant symbiosis (Bodenhausen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, understanding the factors shaping fungal communities within soil environments is paramount for elucidating biological processes and refining soil management practices (Fierer, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Diao et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the core of this research lies the relationship between soil physicochemical properties and fungal density a nexus embodying the intricate interplay between abiotic and biotic factors governing soil microbial dynamics. Soil attributes, encompassing pH, texture, nutrient availability, and organic matter content, profoundly influence microbial communities, fungi included. The spatial distribution of fungal populations within soil intricately intertwines with these physicochemical attributes, delineating the heterogeneous nature of soil habitats and the diverse ecological niches they offer (Canini et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Trivedi et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bodenhausen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsequently, meticulous selection of soil sampling methods emerges as a pivotal determinant in unraveling the complexities of soil fungal ecology (Huang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Disparate sampling techniques may yield divergent insights into fungal community structure and abundance, underscoring the necessity of standardized protocols to ensure data comparability and robustness (Guo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, soil texture emerges as a chief driver in shaping fungal habitats and community composition (Grandy et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Soil structure, particularly the physical arrangement of soil particles, dictates moisture retention, aeration, and nutrient availability, thereby delineating ecological niches suitable for different fungal taxa (Bardgett \u0026amp; van der Putten, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Fine-textured soils, rich in clay content, often host distinct fungal populations compared to coarse, sandy soils, reflecting adaptations to diverse environmental conditions (Grandy et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Iqbal et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we delve into the intricate relationship between soil physicochemical properties and fungal density across diverse agroecosystems in Tunisian regions. Through precise soil sampling, comprehensive physical and chemical analyses, and fungal density assessments, we endeavor to unveil the underlying mechanisms steering fungal community dynamics and their implications for soil health and ecosystem resilience.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eSoil sampling methods\u003c/p\u003e \u003cp\u003eTen fields across four Tunisian regions (Gabes, Kairouan, Sidi Bouzid, and Gafsa) were targeted for soil sampling (refer to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for detailed characterization). At each site, a randomized zigzag transect was established, with samples collected at 2-meter intervals using a 7-cm-diameter soil auger at 10\u0026ndash;30 cm depth. Ninety individual cores were combined to create each composite sample, with three replicates collected per species (approximately 200 g each). Twenty-seven samples in total were collected for each species and transported to the laboratory in sterile polythene bags.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCrops species and varieties along with their place of collection sampled in season from 2022 to 2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVarieties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlace of collection\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePunica granatum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGabsi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOasis of Chott Sidi Abdel Salam, Gabes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhoenix dactylifera\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBouhattam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOasis of Chenini, Gabes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOlea europaea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChetoui\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChrarda, Kairouan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVitis vinifera\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVictoria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegueb, Sidi Bouzid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrunus persica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarioca\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSidi Aich, Gafsa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSolanum lycopersicum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaadha\u003c/p\u003e \u003cp\u003eFirenze\u003c/p\u003e \u003cp\u003eDorra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSidi Ali Ben Aoun, Sidi Bouzid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSolanum tuberosum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpunta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBir El Hafey, Sidi Bouzid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSoil physical and chemical properties\u003c/p\u003e \u003cp\u003eTwenty-seven air-dried soil samples for each species were sieved (2 mm) to remove debris, followed by physicochemical analysis including pH, electrical conductivity, bulk density, organic carbon/matter, total nitrogen, C/N ratio, texture, total limestone, available phosphorus, and exchangeable potassium, employing respective titration methods.\u003c/p\u003e \u003cp\u003eSoil pH\u003c/p\u003e \u003cp\u003eSoil pH was determined using a 1:2.5 soil:liquid ratio suspension. Five-gram soil samples were mixed with 12.5 mL distilled water and vortexed for 5\u0026ndash;6 min. The suspension was then allowed to equilibrate for 20 min. Subsequently, the pH electrode of a Consort C1010 digital pH meter was inserted directly into the supernatant, and the pH value was recorded (Zhang et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eElectrical conductivity\u003c/p\u003e \u003cp\u003eFollowing pH measurement, the same 1:2.5 soil extract was used for electrical conductivity (EC) determination. An Equiptronic's digital conductivity bridge (Consort C1010) measured the supernatant conductivity after calibration with a 0.01 M KCl solution, following the method of Ratshiedana et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBulk density\u003c/p\u003e \u003cp\u003eSoil bulk density was determined through the cylinder method. Soil samples are collected in cylinders, trimmed to uniform height, and weighed fresh. After oven-drying, samples are re-weighed. Finally, bulk density is computed for each sample as dry weight divided by volume (Diao et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The soil bulk density was calculated using the following formula (Diao et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e):\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u0026#120588;\u0026#119887; = \u0026#119872;/\u0026#119881;\u003c/h2\u003e \u003cp\u003ewhere \u0026#120588;\u0026#119887; is bulk density (g cm\u003csup\u003e\u0026minus;3\u003c/sup\u003e), \u0026#119872; is mass of oven-dry soil (g) and \u0026#119881; is the volume of soil (cm\u003csup\u003e3\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eOrganic carbon and organic matter\u003c/p\u003e \u003cp\u003eSoil organic carbon (OC) was determined by the Walkley-Black method (0.1 g soil, 10 ml 1 N K\u003csub\u003e2\u003c/sub\u003eCr\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e7\u003c/sub\u003e, 20 ml H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, cooling, 5 ml H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, 25 ml H\u003csub\u003e2\u003c/sub\u003eO, diphenylamine indicator, titration with 0.5 N Fe(NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e(SO\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e endpoint). A blank control was included. Organic matter (OM) was subsequently estimated using a 1.724 conversion factor based on the assumption of 58% carbon content (OM\u0026thinsp;=\u0026thinsp;OC x 1.724) (Rhouma et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEstimation of total nitrogen (N) and carbon and nitrogen ratio (C/N)\u003c/p\u003e \u003cp\u003eTotal soil nitrogen-supplying potential was assessed by alkaline potassium permanganate distillation, measuring liberated ammonia (hydrolyzed amino-N) as an index of nitrogen status. Available N was estimated by the Kjeldahl method (20 g soil, 0.32% KMnO\u003csub\u003e4\u003c/sub\u003e, 25% NaOH, distillation, boric acid/indicator trap). The pinkish color turning green upon ammonia absorption indicated completion, followed by titration with 0.02 N H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e to the original pink for N quantification (Kjeldahl, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1883\u003c/span\u003e). Additionally, soil C/N ratio (carbon and nitrogen ratio) was calculated according to Zhang et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil texture\u003c/p\u003e \u003cp\u003eSoil texture, defined as the relative proportions of sand (2-0.05 mm), silt (0.05\u0026thinsp;\u0026minus;\u0026thinsp;0.002 mm), and clay (\u0026lt;\u0026thinsp;0.002 mm), was determined using the Robinson pipette method following wet sieving for sand fractions. Fine material suspensions underwent sedimentation analysis with pipette extraction at predetermined depths, dried at 105\u0026deg;C, and quantified to classify soil texture based on the textural triangle (Jackson, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Mathieu and Pieltain, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAvailable total limestone\u003c/p\u003e \u003cp\u003eAvailable total limestone in soil samples was estimated using Bernard's calcimeter by measuring CO\u003csub\u003e2\u003c/sub\u003e evolution upon the addition of 50% HCl. This analysis monitored the CO\u003csub\u003e2\u003c/sub\u003e released from a known amount of pure, dry CaCO\u003csub\u003e3\u003c/sub\u003e reacting with a precisely weighed soil sample, based on the principle of reaction stoichiometry (Petard, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAvailable phosphorus\u003c/p\u003e \u003cp\u003eSodium bicarbonate-extractable phosphorus (P) was determined in 2.5 g soil samples shaken with 20 mL 2% solution and charcoal for 30 minutes. After filtration, 5 mL aliquots were reacted with a composite reagent (5 mL of 3% ammonium molybdate, 12.5 mL of sulfuric acid, 5 mL of ascorbic acid, and 25 mL of potassium antimony tartrate), and incubated for 5 min, and the resulting blue color development quantified at 660 nm using UV spectrophotometry (Bray \u0026amp; Kurtz, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1945\u003c/span\u003e; Olsen et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1954\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAvailable potassium\u003c/p\u003e \u003cp\u003eSoil available potassium was analyzed through a chemical extraction method (10 g soil, 40 mL 2% ammonium acetate, 1 hour shaking, filtration). The filtrate volume was adjusted to 40 mL, and potassium concentration was measured by flame photometry after standardization and calibration with potassium solutions (10, 15, and 40 ppm) (Van Rast et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDistribution of soil fungal community and \u003cem\u003eTrichoderma\u003c/em\u003e spp.\u003c/p\u003e \u003cp\u003eA soil suspension (10 g soil in 90 mL sterile water) underwent serial dilutions (10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e) for fungal isolation via the dilution-plate method. Aliquots (0.1 mL) were plated onto PDA media and incubated in the dark at 25\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C for 15\u0026ndash;21 days (Matrood et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cem\u003eTrichoderma\u003c/em\u003e spp. identification relied on sequential sub-culturing for purification, followed by phenotypic examination (colony morphology, microscopic features of mycelium, conidiophores, conidia, and sexual forms) with cotton blue mounts, utilizing identification keys (Domsch et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Kirk \u0026amp; Ansell, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Taxonomic assignments were validated against Index Fungorum (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://orcid.org/0000-0001-6074-0076\" target=\"_blank\"\u003ewww.indexfungorum.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.indexfungorum.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo estimate fungal and \u003cem\u003eTrichoderma\u003c/em\u003e spp. populations, Mouria et al. (2012)'s formula was adopted. Plates with 30\u0026ndash;300 colonies at consecutive dilutions were retained. Colony forming units (CFU/g soil) were calculated as the total colony count divided by 0.1 multiplied by the sum of retained plates at each dilution, then multiplied by the dilution factor.\u003c/p\u003e \u003cp\u003eColony forming units of the fungal population (CFU/g soil) were calculated as: (Total colonies) / (0.1 * (Σ retained plates for the first and second dilutions) * dilution factor), where Σ denotes the sum of retained plates at each dilution.\u003c/p\u003e \u003cp\u003eColony forming units of \u003cem\u003eTrichoderma\u003c/em\u003e spp. (CFU/g soil) were calculated as: (Total colonies of \u003cem\u003eTrichoderma\u003c/em\u003e spp.) / (0.1 * (Σ retained plates for the first and second dilutions) * dilution factor), where Σ denotes the sum of retained plates at each dilution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe experiment employed a one-way analysis of variance (ANOVA) to assess variations among treatment groups. Data from replicates were averaged, and the resulting means were subjected to the ANOVA using SPSS version 20.0 software. Before the ANOVA, normality, and homogeneity of variance assumptions were verified using tests like Duncan's Multiple Range Test. This same test (Duncan's Multiple Range Test) was subsequently employed to identify statistically significant differences (P\u0026thinsp;\u0026le;\u0026thinsp;0.05) between treatment means.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSoil physical and chemical properties\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarized the physicochemical properties of various soil samples collected around different vegetable species. The analysis revealed significant differences in these properties across the crops (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Soil pH ranges from slightly acidic (\u003cem\u003eP. persica\u003c/em\u003e cv. Carioca; 6.49) to moderately alkaline (\u003cem\u003eP. dactylifera\u003c/em\u003e cv. Bouhattam; 8.69). Electrical conductivity, a measure of salt content, is highest in \u003cem\u003eS. lycopersicum\u003c/em\u003e cv. Dorra (2.92 ds.m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and lowest in \u003cem\u003eP. granatum\u003c/em\u003e cv. Gabsi (1.30 ds.m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Bulk density, an indicator of soil compaction, is highest in \u003cem\u003eS. tuberosum\u003c/em\u003e cv. Spunta (2.71 g.cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) and lowest in \u003cem\u003eP. granatum\u003c/em\u003e cv. Gabsi (1.34 g.cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of soil physicochemical properties at different vegetables species\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEC (ds.m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBD (g.cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOC (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOM (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eClay (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSilt (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSand (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eTL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eP\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e (ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eK\u003csub\u003e2\u003c/sub\u003eO (ppm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePunica granatum\u003c/em\u003e cv. Gabsi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07f\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e69.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e801\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhoenix dactylifera\u003c/em\u003e cv. Bouhattam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02de\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07de\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e70.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e9.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e893.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOlea europaea\u003c/em\u003e cv. Chetoui\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e55.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.91b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e605.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52bcd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVitis vinifera\u003c/em\u003e cv. Victoria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e48.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e659.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98bc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrunus persica\u003c/em\u003e cv. Carioca\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69abc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e45.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e8.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e704.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72abc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSolanum lycopersicum\u003c/em\u003e cv. Saadha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02ef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02ef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e50.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e42.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26de\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46bcd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e554.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18cd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. lycopersicum\u003c/em\u003e cv. Firenze\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e53.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e42.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76de\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e449\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. lycopersicum\u003c/em\u003e cv. Dorra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e52.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e42.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37de\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e622.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29bcd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. tuberosum\u003c/em\u003e cv. Spunta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07fg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01fg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e38.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49bcd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e766.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.09ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003csup\u003ea\u003c/sup\u003eMeans\u0026plusmn; standard error in a column followed by the same letter are not significantly different according to Duncan\u0026rsquo;s Multiple Range Test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003csup\u003eb\u003c/sup\u003eProbabilities associated with individual F tests.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003eEC: Electrical conductivity; BD: Bulk density; OC: Organic carbon; OM: Organic matter; TN: Total nitrogen; C/N: Carbon/total nitrate ratio; TL: Total limestone.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAn examination of soil composition across various crops revealed potential variations in irrigation needs and inherent nutrient availability. Organic carbon and organic matter content, crucial for nutrient retention, are highest in \u003cem\u003eP. granatum\u003c/em\u003e cv. Gabsi (0.88 and 1.52%, respectively) and lowest in \u003cem\u003eS. lycopersicum\u003c/em\u003e cv. Firenze and cv. Dorra (0.05 and 0.09%, respectively). Total nitrogen follows a similar trend. The C/N ratio, reflecting nutrient availability, is highest in \u003cem\u003eO. europaea\u003c/em\u003e cv. Chetoui (9.03) and lowest in \u003cem\u003eS. lycopersicum\u003c/em\u003e cv. Dorra (1.23). Clay content is highest in \u003cem\u003eS. tuberosum\u003c/em\u003e cv. Spunta (57.99%) and lowest in \u003cem\u003eP. dactylifera\u003c/em\u003e cv. Bouhattam (21.31%), while sand content shows the opposite trend (3.28 and 8.23%, respectively). The high sand content observed in most species suggests these soils may have good drainage properties. This could necessitate adjustments to watering regimes to prevent underwatering. Conversely, crops like tomatoes (42% sand) and potatoes (38.73% sand) with lower sand content might require more frequent, controlled irrigation to avoid waterlogging due to potentially higher water retention capacity. Furthermore, analysis of total limestone content displayed variation between crops, ranging from 1.17% in \u003cem\u003eP. granatum\u003c/em\u003e cv. Gabsi to 2.18% in \u003cem\u003eS. tuberosum\u003c/em\u003e cv. Spunta. Finally, the analysis of mineral nutrients, specifically P\u003csub\u003e2\u003c/sub\u003eO5 and K\u003csub\u003e2\u003c/sub\u003eO, revealed significant differences between the crops. \u003cem\u003eP. dactylifera\u003c/em\u003e cv. Bouhattam demonstrated the highest levels of both P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e (9.04 ppm) and K\u003csub\u003e2\u003c/sub\u003eO (893.67 ppm), potentially indicating a naturally richer soil environment for this particular cultivar. On the other hand, \u003cem\u003eO. europaea\u003c/em\u003e cv. Chetoui (P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e; 6.03 ppm) and \u003cem\u003eS. lycopersicum\u003c/em\u003e cv. Firenze (K\u003csub\u003e2\u003c/sub\u003eO; 449 ppm) exhibited the lowest levels of these nutrients, suggesting a potential need for additional fertilization for optimal growth. This data highlights the importance of considering individual crop characteristics and soil composition when developing targeted irrigation and nutrient management strategies (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDistribution of soil fungal community\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e revealed significant variations in the abundance of fungal communities across the soil samples collected around different vegetable species. There's a statistically significant difference between fungal abundance in all the compared crops (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Fungal colony forming units (CFU) per gram of soil serve as a proxy for fungal abundance. \u003cem\u003eP. granatum\u003c/em\u003e cv. Gabsi exhibited the most abundant fungal community (14.82 x 10\u003csup\u003e5\u003c/sup\u003e CFU/g), while \u003cem\u003eS. lycopersicum\u003c/em\u003e (cvs. Firenze and Dorra) have the least (0.92 x 10\u003csup\u003e5\u003c/sup\u003e and 0.66 x 10\u003csup\u003e5\u003c/sup\u003e CFU/g, respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of soil fungal community\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFungal community (10\u003csup\u003e5\u003c/sup\u003e CFU/g of soil)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePunica granatum\u003c/em\u003e cv. Gabsi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28a\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhoenix dactylifera\u003c/em\u003e cv. Bouhattam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOlea europaea\u003c/em\u003e cv. Chetoui\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVitis vinifera\u003c/em\u003e cv. Victoria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrunus persica\u003c/em\u003e cv. Carioca\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSolanum lycopersicum\u003c/em\u003e cv. Saadha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55f\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. lycopersicum\u003c/em\u003e cv. Firenze\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. lycopersicum\u003c/em\u003e cv. Dorra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16i\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. tuberosum\u003c/em\u003e cv. Spunta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48g\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003ea\u003c/sup\u003eMeans\u0026plusmn; standard error in a column followed by the same letter are not significantly different according to Duncan\u0026rsquo;s Multiple Range Test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003eb\u003c/sup\u003eProbabilities associated with individual F tests.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDistribution of \u003cem\u003eTrichoderma\u003c/em\u003e spp.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e highlighted a significant variation in the abundance of \u003cem\u003eTrichoderma\u003c/em\u003e spp. across the investigated vegetable crops (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). \u003cem\u003eP. granatum\u003c/em\u003e cv. Gabsi displays the highest abundance (2.02 x 10\u003csup\u003e5\u003c/sup\u003e CFU/g), whereas \u003cem\u003eS. lycopersicum\u003c/em\u003e (cvs. Firenze and Dorra) have the lowest (0.04 x 10\u003csup\u003e5\u003c/sup\u003e and 0.06 x 10\u003csup\u003e5\u003c/sup\u003e CFU/g, respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of \u003cem\u003eTrichoderma\u003c/em\u003e spp.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTrichoderma\u003c/em\u003e spp. (10\u003csup\u003e5\u003c/sup\u003e CFU/g of soil)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePunica granatum\u003c/em\u003e cv. Gabsi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05a\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhoenix dactylifera\u003c/em\u003e cv. Bouhattam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOlea europaea\u003c/em\u003e cv. Chetoui\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVitis vinifera\u003c/em\u003e cv. Victoria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrunus persica\u003c/em\u003e cv. Carioca\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSolanum lycopersicum\u003c/em\u003e cv. Saadha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. lycopersicum\u003c/em\u003e cv. Firenze\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01f\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. lycopersicum\u003c/em\u003e cv. Dorra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01f\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. tuberosum\u003c/em\u003e cv. Spunta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01f\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003ea\u003c/sup\u003eMeans\u0026plusmn; standard error in a column followed by the same letter are not significantly different according to Duncan\u0026rsquo;s Multiple Range Test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003eb\u003c/sup\u003eProbabilities associated with individual F tests.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFactors associated with soil fungal community in soil\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e delved into the intricate connections between soil physicochemical properties and the distribution of soil fungal communities. Fungal abundance (CFU) exhibited a strong negative correlation with electrical conductivity (r = -0.883), bulk density (r = -0.895), clay content (r = -0.652), and total limestone (r = -0.821). This implies that fungal communities are less prevalent in soils with high levels of dissolved salts, compaction, and clay textures. On the other hand, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e highlighted a robust positive correlation between fungal CFU and organic carbon (r\u0026thinsp;=\u0026thinsp;0.941), organic matter (r\u0026thinsp;=\u0026thinsp;0.941), total nitrogen (r\u0026thinsp;=\u0026thinsp;0.935), and sand content (r\u0026thinsp;=\u0026thinsp;0.680). This indicates that fungal communities increased in soil environments enriched with organic carbon and matter, characterized by fertile and well-draining soil with a good balance of nutrients. Additionally, the significant positive correlation between the C/N ratio (r\u0026thinsp;=\u0026thinsp;0.686) and fungal abundance implies that fungal communities prefer soils with a higher ratio of organic carbon to total nitrogen, potentially due to a readily available carbon source for their growth (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation coefficients between soil physicochemical properties and distribution of soil fungal community\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSilt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSand\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eTL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eP\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eK\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.883\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.895\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.941\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.941\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.935\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.686\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.652\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.680\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.821\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.398\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.446\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.782\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.840\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.840\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.746\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.746\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e 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align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.896\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.815\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.620\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.652\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.828\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e 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\u003cp\u003e\u0026minus;\u0026thinsp;.886\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.560\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.512\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003csup\u003ea\u003c/sup\u003eMeans\u0026plusmn; standard error in a column followed by the same letter are not significantly different according to Duncan\u0026rsquo;s Multiple Range Test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003csup\u003eb\u003c/sup\u003eProbabilities associated with individual F tests.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003eEC: Electrical conductivity; BD: Bulk density; OC: Organic carbon; OM: Organic matter; TN: Total nitrogen; C/N: Carbon/total nitrate ratio; TL: Total limestone.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA) revealed a significant influence of soil physicochemical properties, soil fungal community composition, and \u003cem\u003eTrichoderma\u003c/em\u003e spp. density on the observed effects. The first two principal components (PC1 and PC2) explained a combined variance of 83.237% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). PC1 contributed the most, accounting for 63.123% of the total variance. This component exhibited a positive correlation with the soil fungal community structure, \u003cem\u003eTrichoderma\u003c/em\u003e spp. density, total nitrogen, organic carbon, organic matter content, and the C/N ratio. Conversely, PC2, explaining 20.114% of the variance, showed positive correlations with silt content, potassium (K₂O), phosphorus (P₂O₅), and soil pH (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSoil represents a complex system arising from interactions between a highly variable physicochemical matrix and diverse assemblages of organisms and nutrients. Natural processes can disrupt this intricate balance, leading to the degradation of soil properties and its ability to support biomass production. Effective management of soil nutrients hinges on understanding the soil-microbial community-plant system. This can be achieved by quantifying the soil proprieties and microbial densities, allowing for the establishment of a balanced relationship between these components for optimal ecosystem function (Rhouma et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rhouma et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Iqbal et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study likely investigated the relationship between different vegetable crops and the fungal communities in the soil, particularly \u003cem\u003eTrichoderma\u003c/em\u003e spp. The findings suggest that the composition and abundance of fungi in the soil varied depending on the vegetable crop planted. Interestingly, the pomegranate cultivar Gabsi was observed to have the highest abundance of fungi, while tomato varieties Firenze and Dorra showed the least. This indicates that different vegetable crops might influence the type and amount of fungi present in the surrounding soil. In simpler terms, the type of vegetables you plant might affect the types of fungi living in the soil around them (Rhouma et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rhouma et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Janowski \u0026amp; Leski, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePlant communities play a key role in shaping the composition and diversity of soil fungal communities through both direct and indirect interactions (Burke et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Rousk and B\u0026aring;\u0026aring;th, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tedersoo et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Plants directly interact with specific fungal groups, such as mycorrhizal and pathogenic fungi. Additionally, they indirectly modify the local soil environment by releasing carbohydrates through their roots (Moll et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Janowski \u0026amp; Leski, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Plant litter deposition also influences soil acidity and nutrient content (Aponte et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), while dead wood and organic matter contribute further organic material (M\u0026auml;kip\u0026auml;\u0026auml; et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The varying degrees of plant interaction among different soil fungal trophic guilds leads to differential distribution patterns within the soil community (Tedersoo et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, the leaf litter can decrease local soil pH, hindering the activity of many fungal taxa (Tedersoo et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Janowski \u0026amp; Leski, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePlant-microbe interactions and niche adaptations significantly influence fungal community composition across the rhizosphere. Plant root exudates containing signaling molecules (flavonoids, strigolactones) and various organic compounds (organic acids, amino acids, proteins, fatty acids) shape these interactions (Sundin \u0026amp; Jacobs, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Zhalnina et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, factors like temperature, oxygen levels, and UV light play a role, and these factors vary considerably across locations, plant root structures, and plant types themselves (P\u0026otilde;lme et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe rhizoplane, the root surface zone directly influenced by root exudates, exhibits lower fungal diversity and richness compared to the surrounding rhizosphere soil. This suggests selective pressure from plant roots limits the number of colonizing species (Lee et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, the fungal communities within each rhizo-compartment (rhizoplane vs. rhizosphere) are distinct and susceptible to further manipulation by host-controlled processes (Gottel et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Janowski \u0026amp; Leski, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Potential variations in communication and mutualistic relationships between plants and fungi might also exist between these compartments. For example, mycorrhizal fungi interacting with root cortical cells may influence the colonization of other fungal species within the rhizosphere (Parniske, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Iqbal et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The interplay of plant immunity and microbial competition likely exerts different selective pressures on the root surface compared to the bulk soil. This phenomenon could ultimately lead to the development of unique fungal communities adapted specifically to the rhizoplane environment (Parniske, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Zamioudis et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Stringlis et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Iqbal et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite their crucial role as plant symbionts, fungal endophytes (\u003cem\u003eTrichoderma\u003c/em\u003e spp.) remain a relatively unexplored component of the soil fungal community. Due to their recent rise in scientific interest, limited research exists on the factors shaping their distribution patterns. While most fungal endophytes exhibit generalist tendencies, colonizing a wide range of plant taxa (Suryanarayanan et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Janowski \u0026amp; Leski, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). U\u0026rsquo;Ren et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) suggested that plant host identity at the clade level might influence their soil distribution.\u003c/p\u003e \u003cp\u003eOur findings demonstrate a significant influence of tomato variety on the associated fungal community. Our finding is in concordance with previous studies in maize (\u003cem\u003eZea mays\u003c/em\u003e) where variety did not significantly impact alpha diversity (species richness within a site) but did influence beta diversity (species composition differences between sites) to some extent (Kong et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similar to our results, potato (\u003cem\u003eSolanum tuberosum\u003c/em\u003e) variety has been shown to significantly affect fungal community composition (Hannula et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kong et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Plant root secretions provide carbon substrates, including primary and secondary metabolites, utilized by fungi (Jones et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Broeckling et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This suggests that different plant varieties may influence the resident soil fungal community through variations in their root exudate composition. This aligns with prior research demonstrating the ability of diverse plant species to shape fungal communities via root secretions. The observed differences in fungal communities associated with different tomato varieties potentially stem from this mechanism (Jones et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Broeckling et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies have shown a strong correlation between changes in soil environmental factors and soil microbial biomass (Zhao et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Particularly, research suggests that improvements in soil porosity and moisture content are key drivers of increased microbial biomass and activity (Hu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Enhanced porosity is believed to improve soil aeration, creating a more optimal environment for soil microorganisms by facilitating gas exchange and potentially mitigating limitations caused by low oxygen availability (Zhao et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Soil compaction, often caused by heavy machinery or unsustainable cropping practices, negatively impacts soil physical properties, potentially hindering microbial activity and essential biochemical processes crucial for nutrient availability. Research demonstrates a linear decline in microbial populations with increasing soil bulk density (Li et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Li et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) observed a 26\u0026ndash;39% decrease in microbial communities when soil bulk density increased from 1.00 to 1.60 mg m-3. These findings align with studies by Landina and Klevenskaya (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) and Smeltzer et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) showing a reduction in microbial biomass carbon with compaction, which strongly correlates with microbial numbers. While plate counting methods used only capture a fraction of the total soil microbial community, the significant decrease in measured populations underscores the detrimental effect of soil compaction on microbial activity (Torsvik et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil texture, defined by the relative proportions of sand, silt, and clay, is a well-established factor influencing soil microbial communities (Fierer, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Finer textured soils, with higher clay content, tend to have a greater surface area and water-holding capacity (Mathieu \u0026amp; Pieltain, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Lucas et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This creates more favorable conditions for a wider variety and abundance of microbes. Conversely, coarse-textured soils with high sand content offer less surface area and limited water retention, potentially limiting microbial diversity and favoring microbes adapted to drier environments. Ultimately, soil texture influences the physical environment that soil microbes inhabit, impacting factors like oxygen availability, nutrient absorption, and water accessibility, all of which play a crucial role in determining the types and overall activity of the soil microbial community (Grandy et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lucas et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Diao et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil pH acts as a key environmental factor influencing the composition and function of microbial communities. It can directly impact microbial activity by altering the efficiency and functionality of enzymes critical for various metabolic processes. Additionally, pH indirectly affects microbial communities by influencing the solubility and availability of essential nutrients such as phosphorus and potassium. This altered nutrient profile can then selectively favor specific microbial taxa with adaptations to grow under those conditions. Studies by Bodenhausen et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) support this notion, demonstrating a significant influence of potassium and phosphorus on fungal community structure in arable soils. Similarly, research by Fierer \u0026amp; Jackson (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) has established the well-known role of nitrogen and phosphorus availability in shaping the composition of microbial communities.\u003c/p\u003e \u003cp\u003eSoil characteristics including pH, nitrate nitrogen content, organic carbon level, and clay content have been identified as key factors shaping fungal community composition (Huang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This aligns with previous research highlighting the prominent roles of pH and organic carbon in structuring overall soil fungal communities (Grandy et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Organic matter amendments, by optimizing soil structure, demonstrably promote fungal abundance (Lucas et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, factors like soil salinity and land-use type exert significant influence on the composition and structure of fungal communities (Rath et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Land-use type or past land management practices have also been shown to play a part in shaping fungal community structure (Ma et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This collective evidence underscores the multifaceted nature of factors influencing fungal communities in soil ecosystems (Huang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAciego Pietri et al. (2008) and Deng et al. (2017) observed an inverse relationship between soil pH, total nitrogen content, and the composition of microbial communities, which aligns with previous research. This suggests that lower pH and higher total nitrogen levels influence microbial community structure (Li et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). organic matter emerges as a key factor shaping fungal communities, as evidenced by its positive correlation with numerous fungal species (Wei et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, findings by Sun et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) on total nitrogen, organic carbon, and organic matter mirrored the present study's observations. Network analysis revealed positive correlations between total nitrogen and 30 fungal species, while organic matter showed a similar positive association with 27 species, many of which overlapped with those linked to total nitrogen. In contrast, available phosphorus only correlated positively with 12 fungal species (Billah et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These findings, along with those of Ma et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), suggest that environmental factors like organic matter, total nitrogen, and available phosphorus promote the growth and reproduction of diverse fungal communities, whereas pH acts as a potential inhibitor for many microbial taxa. Notably, variations in nitrogen, phosphorus, and potassium availability are crucial for microbial development and reproduction (Billah et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In conclusion, the interplay between environmental factors and microbial communities is complex. These factors can directly and indirectly influence the composition and development of microbial communities, impacting nutrient cycling and overall soil quality (Ma et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study confirms previous findings (Cline et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Canini et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) that both soil properties and plant communities are key factors shaping fungal community composition in the soil. Soil properties directly influence fungal communities by providing essential nutrients, with variations in these properties leading to shifts in fungal composition (Yu et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Plant communities likely play a similar role due to their complex interactions with soil fungi. For instance, plant pathogens rely heavily on specific plant hosts (Nanjundappa et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, interactions occur among the different fungal groups themselves. Symbiotic fungi and plant pathogens often exhibit mutual inhibition (Li et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), ultimately influencing the overall fungal community composition. Consequently, both soil properties and plant communities significantly impact not just the total fungal composition but also the functional roles played by different fungal ecological groups within the soil (Rodriguez-Ramos et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Given these intricate interactions, further research is warranted to explore the potential triangular relationship between plant communities, soil fungal diversity, and abiotic factors.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study revealed a strong link between soil health and fungal communities in Tunisian agroecosystems. Variations in soil properties, including pH, salinity, compaction, organic matter, and nutrients, significantly impacted fungal abundance and composition across different crops. This emphasizes the need for tailored irrigation and fertilization practices to optimize both plant growth and potentially beneficial fungal communities. The observed preference of fungi for well-aerated, fertile soils with readily available carbon underscores the importance of maintaining good soil health for fostering diverse and abundant fungal populations. Future research could explore how agricultural practices can be further optimized to promote these beneficial soil-fungal relationships for enhanced ecosystem health and agricultural productivity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e The authors are grateful to the review editor and the anonymous reviewers for their helpful comments and suggestions to improve the clarity of the research paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This research was funded by the REVINE (Regenerative agricultural approaches to improve ecosystem services in Mediterranean vineyards) project, affiliated with the PRIMA program\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e All relevant data are within the paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman participants and/or animals\u0026nbsp;\u003c/strong\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all individual participants included in the study. All authors have approved the manuscript for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u0026nbsp;\u003c/strong\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflict of interest.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAciego Pietri, J. C., \u0026amp; Brookes, P. C. (2008). 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Stand-scale spatial patterns of soil microbial biomass in natural cold-temperate beech forests along an elevation gradient. \u003cem\u003eSoil Biology \u0026amp; Biochemistry\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e, 1466\u0026ndash;1474. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2009.03.028\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2009.03.028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Soil fungal densities, Soil properties, Trichoderma spp., Crops","lastPublishedDoi":"10.21203/rs.3.rs-4332094/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4332094/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil fungi are vital members of the soil ecosystem, performing a multitude of functions critical for ecosystem health. This study examined the relationship between soil properties and fungal communities in Tunisian agroecosystems. Soil characteristics like pH, electrical conductivity, bulk density, and nutrient content displayed significant variations across the studied crops. These variations suggest the need for tailored irrigation and fertilization practices for optimal plant growth. Fungal abundance also varied significantly, with pomegranate (\u003cem\u003eP. granatum\u003c/em\u003e cv. Gabsi: 14.82 x 10⁵ CFU/g of soil) harboring the most abundant community, while tomato (\u003cem\u003eS. lycopersicum\u003c/em\u003e cvs. Firenze and Dorra: 0.92 x 10⁵ and 0.66 x 10⁵ CFU/g of soil, respectively) exhibited the least. Similarly, \u003cem\u003eTrichoderma\u003c/em\u003e spp. abundance followed the same pattern (2.02 x 10⁵, 0.04 x 10⁵, and 0.06 x 10⁵ CFU/g of soil, respectively). Analysis revealed that fungal abundance increased in soils with low salinity, compaction, and clay content, but increased more in environments rich in organic matter, nutrients, and well-drained sandy textures. Furthermore, a preference for a higher soil carbon-to-nitrogen ratio suggests fungi favor readily available carbon sources for growth. In Tunisian agroecosystems, soil properties significantly influenced fungal abundance and composition across crops. This highlights the need for tailored management practices to promote both plant growth and beneficial fungi, with a focus on maintaining good soil health for diverse fungal communities.\u003c/p\u003e","manuscriptTitle":"Interaction between soils physicochemical properties and fungal communities in different Tunisian agroecosystems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-14 09:39:12","doi":"10.21203/rs.3.rs-4332094/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e2206190-72a9-4e41-9aa2-95b4fa4aafe2","owner":[],"postedDate":"May 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-30T06:51:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-14 09:39:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4332094","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4332094","identity":"rs-4332094","version":["v1"]},"buildId":"cTy_lsJlmDsVRNrSptgXS","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}